@inproceedings{abhishekMatthewsCorrelationCoefficient2021,
  title = {Matthews Correlation Coefficient Loss for Deep Convolutional Networks: {{Application}} to Skin Lesion Segmentation},
  booktitle = {2021 {{IEEE}} 18th {{International Symposium}} on {{Biomedical Imaging}} ({{ISBI}})},
  author = {Abhishek, Kumar and Hamarneh, Ghassan},
  year = {2021},
  pages = {225--229},
  publisher = {{IEEE}},
  file = {/Users/scholz/Zotero/storage/QQ9LIKTP/Abhishek and Hamarneh - 2021 - Matthews correlation coefficient loss for deep con.pdf}
}

@article{bakasAdvancingCancerGenome2017,
  title = {Advancing {{The Cancer Genome Atlas}} Glioma {{MRI}} Collections with Expert Segmentation Labels and Radiomic Features},
  author = {Bakas, Spyridon and Akbari, Hamed and Sotiras, Aristeidis and Bilello, Michel and Rozycki, Martin and Kirby, Justin S. and Freymann, John B. and Farahani, Keyvan and Davatzikos, Christos},
  year = {2017},
  month = sep,
  journal = {Scientific Data},
  volume = {4},
  number = {1},
  pages = {170117},
  publisher = {{Nature Publishing Group}},
  issn = {2052-4463},
  doi = {10.1038/sdata.2017.117},
  urldate = {2023-11-03},
  abstract = {Gliomas belong to a group of central nervous system tumors, and consist of various sub-regions. Gold standard labeling of these sub-regions in radiographic imaging is essential for both clinical and computational studies, including radiomic and radiogenomic analyses. Towards this end, we release segmentation labels and radiomic features for all pre-operative multimodal magnetic resonance imaging (MRI) (n=243) of the multi-institutional glioma collections of The Cancer Genome Atlas (TCGA), publicly available in The Cancer Imaging Archive (TCIA). Pre-operative scans were identified in both glioblastoma (TCGA-GBM, n=135) and low-grade-glioma (TCGA-LGG, n=108) collections via radiological assessment. The glioma sub-region labels were produced by an automated state-of-the-art method and manually revised by an expert board-certified neuroradiologist. An extensive panel of radiomic features was extracted based on the manually-revised labels. This set of labels and features should enable i) direct utilization of the TCGA/TCIA glioma collections towards repeatable, reproducible and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments, as well as ii) performance evaluation of computer-aided segmentation methods, and comparison to our state-of-the-art method.},
  copyright = {2017 The Author(s)},
  langid = {english},
  keywords = {Cancer imaging,CNS cancer,Computational models,Image processing,Translational research},
  file = {/Users/scholz/Zotero/storage/UH6WC5YI/Bakas et al. - 2017 - Advancing The Cancer Genome Atlas glioma MRI colle.pdf}
}

@article{bakasUniversityPennsylvaniaGlioblastoma2022,
  title = {The {{University}} of {{Pennsylvania}} Glioblastoma ({{UPenn-GBM}}) Cohort: Advanced {{MRI}}, Clinical, Genomics, \& Radiomics},
  shorttitle = {The {{University}} of {{Pennsylvania}} Glioblastoma ({{UPenn-GBM}}) Cohort},
  author = {Bakas, Spyridon and Sako, Chiharu and Akbari, Hamed and Bilello, Michel and Sotiras, Aristeidis and Shukla, Gaurav and Rudie, Jeffrey D. and Santamar{\'i}a, Natali Flores and Kazerooni, Anahita Fathi and Pati, Sarthak and Rathore, Saima and Mamourian, Elizabeth and Ha, Sung Min and Parker, William and Doshi, Jimit and Baid, Ujjwal and Bergman, Mark and Binder, Zev A. and Verma, Ragini and Lustig, Robert A. and Desai, Arati S. and Bagley, Stephen J. and Mourelatos, Zissimos and Morrissette, Jennifer and Watt, Christopher D. and Brem, Steven and Wolf, Ronald L. and Melhem, Elias R. and Nasrallah, MacLean P. and Mohan, Suyash and O'Rourke, Donald M. and Davatzikos, Christos},
  year = {2022},
  month = jul,
  journal = {Scientific Data},
  volume = {9},
  number = {1},
  pages = {453},
  publisher = {{Nature Publishing Group}},
  issn = {2052-4463},
  doi = {10.1038/s41597-022-01560-7},
  urldate = {2023-11-03},
  abstract = {Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of: a) number of subjects, b) lack of consistent acquisition protocol, c) data quality, or d) accompanying clinical, demographic, and molecular information. Toward alleviating these limitations, we contribute the ``University of Pennsylvania Glioblastoma Imaging, Genomics, and Radiomics'' (UPenn-GBM) dataset, which describes the currently largest publicly available comprehensive collection of 630 patients diagnosed with de novo glioblastoma. The UPenn-GBM dataset includes (a) advanced multi-parametric magnetic resonance imaging scans acquired during routine clinical practice, at the University of Pennsylvania Health System, (b) accompanying clinical, demographic, and molecular information, (d) perfusion and diffusion derivative volumes, (e) computationally-derived and manually-revised expert annotations of tumor sub-regions, as well as (f) quantitative imaging (also known as radiomic) features corresponding to each of these regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments.},
  copyright = {2022 The Author(s)},
  langid = {english},
  keywords = {Biomarkers,CNS cancer,Magnetic resonance imaging,Research data,Translational research},
  file = {/Users/scholz/Zotero/storage/3EL5VS5N/Bakas et al. - 2022 - The University of Pennsylvania glioblastoma (UPenn.pdf}
}

@article{bhandariNoninvasiveDeterminationIDH2021,
  title = {Noninvasive {{Determination}} of {{IDH}} and 1p19q {{Status}} of {{Lower-grade Gliomas Using MRI Radiomics}}: {{A Systematic Review}}},
  shorttitle = {Noninvasive {{Determination}} of {{IDH}} and 1p19q {{Status}} of {{Lower-grade Gliomas Using MRI Radiomics}}},
  author = {Bhandari, A. P. and Liong, R. and Koppen, J. and Murthy, S. V. and Lasocki, A.},
  year = {2021},
  month = jan,
  journal = {AJNR. American journal of neuroradiology},
  volume = {42},
  number = {1},
  pages = {94--101},
  issn = {1936-959X},
  doi = {10.3174/ajnr.A6875},
  abstract = {BACKGROUND: Determination of isocitrate dehydrogenase (IDH) status and, if IDH-mutant, assessing 1p19q codeletion are an important component of diagnosis of World Health Organization grades II/III or lower-grade gliomas. This has led to research into noninvasively correlating imaging features ("radiomics") with genetic status. PURPOSE: Our aim was to perform a diagnostic test accuracy systematic review for classifying IDH and 1p19q status using MR imaging radiomics, to provide future directions for integration into clinical radiology. DATA SOURCES: Ovid (MEDLINE), Scopus, and the Web of Science were searched in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Diagnostic Test Accuracy guidelines. STUDY SELECTION: Fourteen journal articles were selected that included 1655 lower-grade gliomas classified by their IDH and/or 1p19q status from MR imaging radiomic features. DATA ANALYSIS: For each article, the classification of IDH and/or 1p19q status using MR imaging radiomics was evaluated using the area under curve or descriptive statistics. Quality assessment was performed with the Quality Assessment of Diagnostic Accuracy Studies 2 tool and the radiomics quality score. DATA SYNTHESIS: The best classifier of IDH status was with conventional radiomics in combination with convolutional neural network-derived features (area under the curve \,= 0.95, 94.4\% sensitivity, 86.7\% specificity). Optimal classification of 1p19q status occurred with texture-based radiomics (area under the curve \,= 0.96, 90\% sensitivity, 89\% specificity). LIMITATIONS: A meta-analysis showed high heterogeneity due to the uniqueness of radiomic pipelines. CONCLUSIONS: Radiogenomics is a potential alternative to standard invasive biopsy techniques for determination of IDH and 1p19q status in lower-grade gliomas but requires translational research for clinical uptake.},
  langid = {english},
  pmcid = {PMC7814803},
  pmid = {33243896},
  keywords = {Adult,Brain Neoplasms,{Chromosomes, Human, Pair 1},Female,Glioma,Humans,Isocitrate Dehydrogenase,Machine Learning,Magnetic Resonance Imaging,Male,Middle Aged,Mutation},
  file = {/Users/scholz/Zotero/storage/KG73ZNQX/Bhandari et al. - 2021 - Noninvasive Determination of IDH and 1p19q Status .pdf}
}

@article{calabreseUniversityCaliforniaSan2022,
  title = {The {{University}} of {{California San Francisco Preoperative Diffuse}}                     {{Glioma MRI Dataset}}},
  author = {Calabrese, Evan and {Villanueva-Meyer}, Javier E. and Rudie, Jeffrey D. and Rauschecker, Andreas M. and Baid, Ujjwal and Bakas, Spyridon and Cha, Soonmee and Mongan, John T. and Hess, Christopher P.},
  year = {2022},
  month = nov,
  journal = {Radiology: Artificial Intelligence},
  volume = {4},
  number = {6},
  pages = {e220058},
  publisher = {{Radiological Society of North America}},
  doi = {10.1148/ryai.220058},
  urldate = {2023-11-03},
  abstract = {Supplemental material is available for this article. Keywords: Informatics, MR Diffusion Tensor Imaging, MR Perfusion, MR Imaging, Neuro-Oncology, CNS, Brain/Brain Stem, Oncology, Radiogenomics, Radiology-Pathology Integration {\textcopyright} RSNA, 2022},
  file = {/Users/scholz/Zotero/storage/XCPK8UDT/Calabrese et al. - 2022 - The University of California San Francisco Preoper.pdf}
}

@inproceedings{caoLearningImbalancedDatasets2019,
  title = {Learning {{Imbalanced Datasets}} with {{Label-Distribution-Aware Margin Loss}}},
  booktitle = {Advances in {{Neural Information Processing Systems}}},
  author = {Cao, Kaidi and Wei, Colin and Gaidon, Adrien and Arechiga, Nikos and Ma, Tengyu},
  year = {2019},
  volume = {32},
  publisher = {{Curran Associates, Inc.}},
  urldate = {2023-10-23},
  abstract = {Deep learning algorithms can fare poorly when the training dataset suffers from heavy class-imbalance but the testing criterion requires good generalization on less frequent classes. We design two novel methods to improve performance in such scenarios. First, we propose a theoretically-principled label-distribution-aware margin (LDAM) loss motivated by minimizing a margin-based generalization bound. This loss replaces the standard cross-entropy objective during training and can be applied with prior strategies for training with class-imbalance such as re-weighting or re-sampling. Second, we propose a simple, yet effective, training schedule that defers re-weighting until after the initial stage, allowing the model to learn an initial representation while avoiding some of the complications associated with re-weighting or re-sampling. We test our methods on several benchmark vision tasks including the real-world imbalanced dataset iNaturalist 2018. Our experiments show that either of these methods alone can already improve over existing techniques and their combination achieves even better performance gains.},
  file = {/Users/scholz/Zotero/storage/9N6FP8F3/Cao et al. - 2019 - Learning Imbalanced Datasets with Label-Distributi.pdf}
}

@article{chiccoAdvantagesMatthewsCorrelation2020,
  title = {The Advantages of the {{Matthews}} Correlation Coefficient ({{MCC}}) over {{F1}} Score and Accuracy in Binary Classification Evaluation},
  author = {Chicco, Davide and Jurman, Giuseppe},
  year = {2020},
  month = jan,
  journal = {BMC Genomics},
  volume = {21},
  number = {1},
  pages = {6},
  issn = {1471-2164},
  doi = {10.1186/s12864-019-6413-7},
  urldate = {2023-11-05},
  abstract = {To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets.},
  keywords = {Accuracy,Binary classification,Biostatistics,Confusion matrices,Dataset imbalance,F1 score,Genomics,Machine learning,Matthews correlation coefficient},
  file = {/Users/scholz/Zotero/storage/KWKWI64F/Chicco and Jurman - 2020 - The advantages of the Matthews correlation coeffic.pdf;/Users/scholz/Zotero/storage/CDGZ2KHS/s12864-019-6413-7.html}
}

@article{chiccoMatthewsCorrelationCoefficient2021,
  title = {The {{Matthews Correlation Coefficient}} ({{MCC}}) Is {{More Informative Than Cohen}}'s {{Kappa}} and {{Brier Score}} in {{Binary Classification Assessment}}},
  author = {Chicco, Davide and Warrens, Matthijs J. and Jurman, Giuseppe},
  year = {2021},
  journal = {IEEE Access},
  volume = {9},
  pages = {78368--78381},
  issn = {2169-3536},
  doi = {10.1109/ACCESS.2021.3084050},
  urldate = {2023-11-05},
  abstract = {Even if measuring the outcome of binary classifications is a pivotal task in machine learning and statistics, no consensus has been reached yet about which statistical rate to employ to this end. In the last century, the computer science and statistics communities have introduced several scores summing up the correctness of the predictions with respect to the ground truth values. Among these scores, the Matthews correlation coefficient (MCC) was shown to have several advantages over confusion entropy, accuracy, F1 score, balanced accuracy, bookmaker informedness, markedness, and diagnostic odds ratio: MCC, in fact, produces a high score only if the majority of the predicted negative data instances and the majority of the positive data instances are correct, and therefore it results being very trustworthy on imbalanced datasets. In this study, we compare MCC with two other popular scores: Cohen's Kappa, a metric that originated in social sciences, and the Brier score, a strictly proper scoring function which emerged in weather forecasting studies. After explaining the mathematical properties and the relationships between MCC and each of these two rates, we report some use cases where these scores generate different values, which lead to discordant outcomes, where MCC provides a more truthful and informative result. We highlight the reasons why it is more advisable to use MCC rather that Cohen's Kappa and the Brier score to evaluate binary classifications.},
  file = {/Users/scholz/Zotero/storage/UXJDJ6XS/Chicco et al. - 2021 - The Matthews Correlation Coefficient (MCC) is More.pdf;/Users/scholz/Zotero/storage/JI3IIJP7/9440903.html}
}

@article{chiccoMatthewsCorrelationCoefficient2021a,
  title = {The {{Matthews}} Correlation Coefficient ({{MCC}}) Is More Reliable than Balanced Accuracy, Bookmaker Informedness, and Markedness in Two-Class Confusion Matrix Evaluation},
  author = {Chicco, Davide and T{\"o}tsch, Niklas and Jurman, Giuseppe},
  year = {2021},
  month = feb,
  journal = {BioData Mining},
  volume = {14},
  number = {1},
  pages = {13},
  issn = {1756-0381},
  doi = {10.1186/s13040-021-00244-z},
  urldate = {2023-11-05},
  abstract = {Evaluating binary classifications is a pivotal task in statistics and machine learning, because it can influence decisions in multiple areas, including for example prognosis or therapies of patients in critical conditions. The scientific community has not agreed on a general-purpose statistical indicator for evaluating two-class confusion matrices (having true positives, true negatives, false positives, and false negatives) yet, even if advantages of the Matthews correlation coefficient (MCC) over accuracy and F1 score have already been shown.In this manuscript, we reaffirm that MCC is a robust metric that summarizes the classifier performance in a single value, if positive and negative cases are of equal importance. We compare MCC to other metrics which value positive and negative cases equally: balanced accuracy (BA), bookmaker informedness (BM), and markedness (MK). We explain the mathematical relationships between MCC and these indicators, then show some use cases and a bioinformatics scenario where these metrics disagree and where MCC generates a more informative response.Additionally, we describe three exceptions where BM can be more appropriate: analyzing classifications where dataset prevalence is unrepresentative, comparing classifiers on different datasets, and assessing the random guessing level of a classifier. Except in these cases, we believe that MCC is the most informative among the single metrics discussed, and suggest it as standard measure for scientists of all fields. A Matthews correlation coefficient close to +1, in fact, means having high values for all the other confusion matrix metrics. The same cannot be said for balanced accuracy, markedness, bookmaker informedness, accuracy and F1 score.},
  keywords = {Balanced accuracy,Binary classification,Bookmaker informedness,Confusion matrix,Machine learning,Markedness,Matthews correlation coefficient},
  file = {/Users/scholz/Zotero/storage/PEILR5HJ/Chicco et al. - 2021 - The Matthews correlation coefficient (MCC) is more.pdf;/Users/scholz/Zotero/storage/DT85737X/s13040-021-00244-z.html}
}

@article{chiccoMatthewsCorrelationCoefficient2023,
  title = {The {{Matthews}} Correlation Coefficient~({{MCC}}) Should Replace the {{ROC~AUC}} as the Standard Metric for Assessing Binary Classification},
  author = {Chicco, Davide and Jurman, Giuseppe},
  year = {2023},
  month = feb,
  journal = {BioData Mining},
  volume = {16},
  number = {1},
  pages = {4},
  issn = {1756-0381},
  doi = {10.1186/s13040-023-00322-4},
  urldate = {2023-11-05},
  abstract = {Binary classification is a common task for which machine learning and computational statistics are used, and the area under the receiver operating characteristic curve~(ROC~AUC) has become the common standard metric to evaluate binary classifications in most scientific fields. The ROC curve has true positive rate~(also called sensitivity or recall) on the y axis and false positive rate on the x axis, and the ROC~AUC can range from 0 (worst result) to 1~(perfect result). The ROC~AUC, however, has several flaws and drawbacks. This score is generated including predictions that obtained insufficient sensitivity and specificity, and moreover it does not say anything about positive predictive value~(also known as precision) nor negative predictive value~(NPV) obtained by the classifier, therefore potentially generating inflated overoptimistic results. Since it is common to include ROC~AUC alone without precision and negative predictive value, a researcher might erroneously conclude that their classification was successful. Furthermore, a given point in the ROC space does not identify a single confusion matrix nor a group of matrices sharing the same MCC value. Indeed, a given (sensitivity, specificity) pair can cover a broad MCC range, which casts doubts on the reliability of ROC~AUC as a performance measure. In contrast, the Matthews correlation coefficient~(MCC) generates a high score in its \$\$[-1; +1]\$\$interval only if the classifier scored a high value for all the four basic rates of the confusion matrix: sensitivity, specificity, precision, and negative predictive value. A high MCC (for example, MCC \$\$=\$\$0.9), moreover, always corresponds to a high ROC~AUC, and not vice versa. In this short study, we explain why the Matthews correlation coefficient should replace the ROC~AUC as standard statistic in all the scientific studies involving a binary classification, in all scientific fields.},
  keywords = {Area under the curve,AUC,Binary classification,Confusion matrix,Data mining,Data science,Matthews correlation coefficient,Receiver operating characteristic curve,ROC,ROC~AUC,Supervised machine learning},
  file = {/Users/scholz/Zotero/storage/M7D76XY7/Chicco and Jurman - 2023 - The Matthews correlation coefficient (MCC) should .pdf;/Users/scholz/Zotero/storage/XKJ5RW8T/s13040-023-00322-4.html}
}

@misc{cholletKeras2015,
  title = {Keras},
  author = {Chollet, Fran{\c c}ois and others},
  year = {2015},
  publisher = {{GitHub}}
}

@article{cluceruImprovingNoninvasiveClassification2022,
  title = {Improving the Noninvasive Classification of Glioma Genetic Subtype with Deep Learning and Diffusion-Weighted Imaging},
  author = {Cluceru, Julia and Interian, Yannet and Phillips, Joanna J. and Molinaro, Annette M. and Luks, Tracy L. and {Alcaide-Leon}, Paula and Olson, Marram P. and Nair, Devika and LaFontaine, Marisa and Shai, Anny and Chunduru, Pranathi and Pedoia, Valentina and {Villanueva-Meyer}, Javier E. and Chang, Susan M. and Lupo, Janine M.},
  year = {2022},
  month = apr,
  journal = {Neuro-Oncology},
  volume = {24},
  number = {4},
  pages = {639--652},
  issn = {1523-5866},
  doi = {10.1093/neuonc/noab238},
  abstract = {BACKGROUND: Diagnostic classification of diffuse gliomas now requires an assessment of molecular features, often including IDH-mutation and 1p19q-codeletion status. Because genetic testing requires an invasive process, an alternative noninvasive approach is attractive, particularly if resection is not recommended. The goal of this study was to evaluate the effects of training strategy and incorporation of biologically relevant images on predicting genetic subtypes with deep learning. METHODS: Our dataset consisted of 384 patients with newly diagnosed gliomas who underwent preoperative MRI with standard anatomical and diffusion-weighted imaging, and 147 patients from an external cohort with anatomical imaging. Using tissue samples acquired during surgery, each glioma was classified into IDH-wildtype (IDHwt), IDH-mutant/1p19q-noncodeleted (IDHmut-intact), and IDH-mutant/1p19q-codeleted (IDHmut-codel) subgroups. After optimizing training parameters, top performing convolutional neural network (CNN) classifiers were trained, validated, and tested using combinations of anatomical and diffusion MRI with either a 3-class or tiered structure. Generalization to an external cohort was assessed using anatomical imaging models. RESULTS: The best model used a 3-class CNN containing diffusion-weighted imaging as an input, achieving 85.7\% (95\% CI: [77.1, 100]) overall test accuracy and correctly classifying 95.2\%, 88.9\%, 60.0\% of the IDHwt, IDHmut-intact, and IDHmut-codel tumors. In general, 3-class models outperformed tiered approaches by 13.5\%-17.5\%, and models that included diffusion-weighted imaging were 5\%-8.8\% more accurate than those that used only anatomical imaging. CONCLUSION: Training a classifier to predict both IDH-mutation and 1p19q-codeletion status outperformed a tiered structure that first predicted IDH-mutation, then 1p19q-codeletion. Including apparent diffusion coefficient (ADC), a surrogate marker of cellularity, more accurately captured differences between subgroups.},
  langid = {english},
  pmcid = {PMC8972294},
  pmid = {34653254},
  keywords = {ADC,Brain Neoplasms,convolutional neural network,deep learning,Deep Learning,Diffusion Magnetic Resonance Imaging,diffusion-weighted imaging,Glioma,glioma subtype,Humans,Isocitrate Dehydrogenase,Magnetic Resonance Imaging,Mutation},
  file = {/Users/scholz/Zotero/storage/VITPDNM2/Cluceru et al. - 2022 - Improving the noninvasive classification of glioma.pdf;/Users/scholz/Zotero/storage/EQT6KAUN/34653254.html}
}

@article{foltyn-dumitruImpactSignalIntensity2023,
  title = {Impact of Signal Intensity Normalization of {{MRI}} on the Generalizability of Radiomic-Based Prediction of Molecular Glioma Subtypes},
  author = {{Foltyn-Dumitru}, Martha and Schell, Marianne and Rastogi, Aditya and Sahm, Felix and Kessler, Tobias and Wick, Wolfgang and Bendszus, Martin and Brugnara, Gianluca and Vollmuth, Philipp},
  year = {2023},
  month = sep,
  journal = {European Radiology},
  issn = {1432-1084},
  doi = {10.1007/s00330-023-10034-2},
  urldate = {2023-11-03},
  abstract = {Radiomic features have demonstrated encouraging results for non-invasive detection of molecular biomarkers, but the lack of guidelines for pre-processing MRI-data has led to poor generalizability. Here, we assessed the influence of different MRI-intensity normalization techniques on the performance of radiomics-based models for predicting molecular glioma subtypes.},
  langid = {english},
  keywords = {Genotype,Glioma,Isocitrate dehydrogenase,Magnetic resonance imaging},
  file = {/Users/scholz/Zotero/storage/MXCT4Q4R/Foltyn-Dumitru et al. - 2023 - Impact of signal intensity normalization of MRI on.pdf}
}

@article{fooMulticlassClassificationBreast2022,
  title = {Multi-Class Classification of Breast Tissue Using Optical Coherence Tomography and Attenuation Imaging Combined via Deep Learning},
  author = {Foo, Ken Y. and Newman, Kyle and Fang, Qi and Gong, Peijun and Ismail, Hina M. and Lakhiani, Devina D. and Zilkens, Renate and Dessauvagie, Benjamin F. and Latham, Bruce and Saunders, Christobel M. and Chin, Lixin and Kennedy, Brendan F.},
  year = {2022},
  month = jun,
  journal = {Biomedical Optics Express},
  volume = {13},
  number = {6},
  pages = {3380--3400},
  publisher = {{Optica Publishing Group}},
  issn = {2156-7085},
  doi = {10.1364/BOE.455110},
  urldate = {2023-10-23},
  abstract = {We demonstrate a convolutional neural network (CNN) for multi-class breast tissue classification as adipose tissue, benign dense tissue, or malignant tissue, using multi-channel optical coherence tomography (OCT) and attenuation images, and a novel Matthews correlation coefficient (MCC)-based loss function that correlates more strongly with performance metrics than the commonly used cross-entropy loss. We hypothesized that using multi-channel images would increase tumor detection performance compared to using OCT alone. 5,804 images from 29 patients were used to fine-tune a pre-trained ResNet-18 network. Adding attenuation images to OCT images yields statistically significant improvements in several performance metrics, including benign dense tissue sensitivity (68.0\&\#x0025; versus 59.6\&\#x0025;), malignant tissue positive predictive value (PPV) (79.4\&\#x0025; versus 75.5\&\#x0025;), and total accuracy (85.4\&\#x0025; versus 83.3\&\#x0025;), indicating that the additional contrast from attenuation imaging is most beneficial for distinguishing between benign dense tissue and malignant tissue.},
  copyright = {{\textcopyright} 2022 Optica Publishing Group},
  langid = {english},
  keywords = {Attenuation coefficient,Biomedical imaging,Deep learning,Image metrics,Imaging techniques,Neural networks},
  file = {/Users/scholz/Zotero/storage/DPIQSAFE/Foo et al. - 2022 - Multi-class classification of breast tissue using .pdf}
}

@article{fooMulticlassClassificationBreast2022a,
  title = {Multi-Class Classification of Breast Tissue Using Optical Coherence Tomography and Attenuation Imaging Combined via Deep Learning},
  author = {Foo, Ken Y. and Newman, Kyle and Fang, Qi and Gong, Peijun and Ismail, Hina M. and Lakhiani, Devina D. and Zilkens, Renate and Dessauvagie, Benjamin F. and Latham, Bruce and Saunders, Christobel M. and Chin, Lixin and Kennedy, Brendan F.},
  year = {2022},
  month = jun,
  journal = {Biomedical Optics Express},
  volume = {13},
  number = {6},
  pages = {3380--3400},
  publisher = {{Optica Publishing Group}},
  issn = {2156-7085},
  doi = {10.1364/BOE.455110},
  urldate = {2023-11-06},
  abstract = {We demonstrate a convolutional neural network (CNN) for multi-class breast tissue classification as adipose tissue, benign dense tissue, or malignant tissue, using multi-channel optical coherence tomography (OCT) and attenuation images, and a novel Matthews correlation coefficient (MCC)-based loss function that correlates more strongly with performance metrics than the commonly used cross-entropy loss. We hypothesized that using multi-channel images would increase tumor detection performance compared to using OCT alone. 5,804 images from 29 patients were used to fine-tune a pre-trained ResNet-18 network. Adding attenuation images to OCT images yields statistically significant improvements in several performance metrics, including benign dense tissue sensitivity (68.0\&\#x0025; versus 59.6\&\#x0025;), malignant tissue positive predictive value (PPV) (79.4\&\#x0025; versus 75.5\&\#x0025;), and total accuracy (85.4\&\#x0025; versus 83.3\&\#x0025;), indicating that the additional contrast from attenuation imaging is most beneficial for distinguishing between benign dense tissue and malignant tissue.},
  copyright = {{\textcopyright} 2022 Optica Publishing Group},
  langid = {english},
  keywords = {Attenuation coefficient,Biomedical imaging,Deep learning,Image metrics,Imaging techniques,Neural networks}
}

@article{gorodkinComparingTwoKcategory2004,
  title = {Comparing Two {{K-category}} Assignments by a {{K-category}} Correlation Coefficient},
  author = {Gorodkin, J.},
  year = {2004},
  month = dec,
  journal = {Computational Biology and Chemistry},
  volume = {28},
  number = {5},
  pages = {367--374},
  issn = {1476-9271},
  doi = {10.1016/j.compbiolchem.2004.09.006},
  urldate = {2023-11-05},
  abstract = {Predicted assignments of biological sequences are often evaluated by Matthews correlation coefficient. However, Matthews correlation coefficient applies only to cases where the assignments belong to two categories, and cases with more than two categories are often artificially forced into two categories by considering what belongs and what does not belong to one of the categories, leading to the loss of information. Here, an extended correlation coefficient that applies to K-categories is proposed, and this measure is shown to be highly applicable for evaluating prediction of RNA secondary structure in cases where some predicted pairs go into the category ``unknown'' due to lack of reliability in predicted pairs or unpaired residues. Hence, predicting base pairs of RNA secondary structure can be a three-category problem. The measure is further shown to be well in agreement with existing performance measures used for ranking protein secondary structure predictions. Server and software is available at http://rk.kvl.dk/},
  keywords = {Matthews correlation coefficient,Protein secondary structure,RNA secondary structure},
  file = {/Users/scholz/Zotero/storage/27SNU46Z/S1476927104000799.html}
}

@article{gronningMultiPepHierarchicalDeep2021,
  title = {{{MultiPep}}: A Hierarchical Deep Learning Approach for Multi-Label Classification of Peptide Bioactivities},
  author = {Gr{\o}nning, Alexander G B and Kacprowski, Tim and Sch{\'e}ele, Camilla},
  year = {2021},
  month = nov,
  journal = {Biology Methods and Protocols},
  volume = {6},
  number = {1},
  pages = {bpab021},
  issn = {2396-8923},
  doi = {10.1093/biomethods/bpab021},
  abstract = {Peptide-based therapeutics are here to stay and will prosper in the future. A key step in identifying novel peptide-drugs is the determination of their bioactivities. Recent advances in peptidomics screening approaches hold promise as a strategy for identifying novel drug targets. However, these screenings typically generate an immense number of peptides and tools for ranking these peptides prior to planning functional studies are warranted. Whereas a couple of tools in the literature predict multiple classes, these are constructed using multiple binary classifiers. We here aimed to use an innovative deep learning approach to generate an improved peptide bioactivity classifier with capacity of distinguishing between multiple classes. We present MultiPep: a deep learning multi-label classifier that assigns peptides to zero or more of 20 bioactivity classes. We train and test MultiPep on data from several publically available databases. The same data are used for a hierarchical clustering, whose dendrogram shapes the architecture of MultiPep. We test a new loss function that combines a customized version of Matthews correlation coefficient with binary cross entropy (BCE), and show that this is better than using class-weighted BCE as loss function. Further, we show that MultiPep surpasses state-of-the-art peptide bioactivity classifiers and that it predicts known and novel bioactivities of FDA-approved therapeutic peptides. In conclusion, we present innovative machine learning techniques used to produce a peptide prediction tool to aid peptide-based therapy development and hypothesis generation.}
}

@article{haixiangLearningClassimbalancedData2017,
  title = {Learning from Class-Imbalanced Data: {{Review}} of Methods and Applications},
  shorttitle = {Learning from Class-Imbalanced Data},
  author = {Haixiang, Guo and Yijing, Li and Shang, Jennifer and Mingyun, Gu and Yuanyue, Huang and Bing, Gong},
  year = {2017},
  month = may,
  journal = {Expert Systems with Applications},
  volume = {73},
  pages = {220--239},
  issn = {0957-4174},
  doi = {10.1016/j.eswa.2016.12.035},
  urldate = {2023-11-05},
  abstract = {Rare events, especially those that could potentially negatively impact society, often require humans' decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective. Five hundred and seventeen related papers that have been published in the past decade were collected for the study. The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering. We reviewed all collected papers from both a technical and a practical point of view. Modeling methods discussed include techniques such as data preprocessing, classification algorithms and model evaluation. For applications, we first provide a comprehensive taxonomy of the existing application domains of imbalanced learning, and then we detail the applications for each category. Finally, some suggestions from the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the imbalanced learning and rare event detection fields.},
  keywords = {Data mining,Imbalanced data,Machine learning,Rare events},
  file = {/Users/scholz/Zotero/storage/X6KGY8HW/S0957417416307175.html}
}

@inproceedings{heDeepResidualLearning2016,
  title = {Deep Residual Learning for Image Recognition},
  booktitle = {Proceedings of the {{IEEE}} Conference on Computer Vision and Pattern Recognition},
  author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  year = {2016},
  pages = {770--778}
}

@inproceedings{hintonStochasticNeighborEmbedding2002,
  title = {Stochastic {{Neighbor Embedding}}},
  booktitle = {Advances in {{Neural Information Processing Systems}}},
  author = {Hinton, Geoffrey E and Roweis, Sam},
  year = {2002},
  volume = {15},
  publisher = {{MIT Press}},
  urldate = {2023-11-06},
  abstract = {We describe a probabilistic approach to the task of placing objects, de- scribed by high-dimensional vectors or by pairwise dissimilarities, in a low-dimensional space in a way that preserves neighbor identities. A Gaussian is centered on each object in the high-dimensional space and the densities under this Gaussian (or the given dissimilarities) are used to define a probability distribution over all the potential neighbors of the object. The aim of the embedding is to approximate this distribu- tion as well as possible when the same operation is performed on the low-dimensional ``images'' of the objects. A natural cost function is a sum of Kullback-Leibler divergences, one per object, which leads to a simple gradient for adjusting the positions of the low-dimensional im- ages. Unlike other dimensionality reduction methods, this probabilistic framework makes it easy to represent each object by a mixture of widely separated low-dimensional images. This allows ambiguous objects, like the document count vector for the word ``bank'', to have versions close to the images of both ``river'' and ``finance'' without forcing the images of outdoor concepts to be located close to those of corporate concepts.},
  file = {/Users/scholz/Zotero/storage/ECMB3B7S/Hinton and Roweis - 2002 - Stochastic Neighbor Embedding.pdf}
}

@article{jaffariNovelFocalPhi2021,
  title = {A Novel Focal Phi Loss for Power Line Segmentation with Auxiliary Classifier {{U-Net}}},
  author = {Jaffari, Rabeea and Hashmani, Manzoor Ahmed and {Reyes-Aldasoro}, Constantino Carlos},
  year = {2021},
  journal = {Sensors},
  volume = {21},
  number = {8},
  pages = {2803},
  publisher = {{MDPI}}
}

@article{johnsonSurveyDeepLearning2019,
  title = {Survey on Deep Learning with Class Imbalance},
  author = {Johnson, Justin M. and Khoshgoftaar, Taghi M.},
  year = {2019},
  month = dec,
  journal = {Journal of Big Data},
  volume = {6},
  number = {1},
  pages = {27},
  issn = {2196-1115},
  doi = {10.1186/s40537-019-0192-5},
  urldate = {2023-10-23},
  abstract = {The purpose of this study is to examine existing deep learning techniques for addressing class imbalanced data. Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in many real-world applications, e.g., fraud detection and cancer detection. Moreover, highly imbalanced data poses added difficulty, as most learners will exhibit bias towards the majority class, and in extreme cases, may ignore the minority class altogether. Class imbalance has been studied thoroughly over the last two decades using traditional machine learning models, i.e. non-deep learning. Despite recent advances in deep learning, along with its increasing popularity, very little empirical work in the area of deep learning with class imbalance exists. Having achieved record-breaking performance results in several complex domains, investigating the use of deep neural networks for problems containing high levels of class imbalance is of great interest. Available studies regarding class imbalance and deep learning are surveyed in order to better understand the efficacy of deep learning when applied to class imbalanced data. This survey discusses the implementation details and experimental results for each study, and offers additional insight into their strengths and weaknesses. Several areas of focus include: data complexity, architectures tested, performance interpretation, ease of use, big data application, and generalization to other domains. We have found that research in this area is very limited, that most existing work focuses on computer vision tasks with convolutional neural networks, and that the effects of big data are rarely considered. Several traditional methods for class imbalance, e.g. data sampling and cost-sensitive learning, prove to be applicable in deep learning, while more advanced methods that exploit neural network feature learning abilities show promising results. The survey concludes with a discussion that highlights various gaps in deep learning from class imbalanced data for the purpose of guiding future research.},
  langid = {english},
  file = {/Users/scholz/Zotero/storage/FAR6WWUA/Johnson and Khoshgoftaar - 2019 - Survey on deep learning with class imbalance.pdf}
}

@inproceedings{kingmaAdamMethodStochastic2015,
  title = {Adam: {{A Method}} for {{Stochastic Optimization}}},
  booktitle = {3rd {{International Conference}} on {{Learning Representations}}, {{ICLR}} 2015, {{San Diego}}, {{CA}}, {{USA}}, {{May}} 7-9, 2015, {{Conference Track Proceedings}}},
  author = {Kingma, Diederik P. and Ba, Jimmy},
  editor = {Bengio, Yoshua and LeCun, Yann},
  year = {2015}
}

@article{koflerBraTSToolkitTranslating2020,
  title = {{{BraTS Toolkit}}: {{Translating BraTS Brain Tumor Segmentation Algorithms Into Clinical}} and {{Scientific Practice}}},
  shorttitle = {{{BraTS Toolkit}}},
  author = {Kofler, Florian and Berger, Christoph and Waldmannstetter, Diana and Lipkova, Jana and Ezhov, Ivan and Tetteh, Giles and Kirschke, Jan and Zimmer, Claus and Wiestler, Benedikt and Menze, Bjoern H.},
  year = {2020},
  journal = {Frontiers in Neuroscience},
  volume = {14},
  pages = {125},
  issn = {1662-4548},
  doi = {10.3389/fnins.2020.00125},
  abstract = {Despite great advances in brain tumor segmentation and clear clinical need, translation of state-of-the-art computational methods into clinical routine and scientific practice remains a major challenge. Several factors impede successful implementations, including data standardization and preprocessing. However, these steps are pivotal for the deployment of state-of-the-art image segmentation algorithms. To overcome these issues, we present BraTS Toolkit. BraTS Toolkit is a holistic approach to brain tumor segmentation and consists of three components: First, the BraTS Preprocessor facilitates data standardization and preprocessing for researchers and clinicians alike. It covers the entire image analysis workflow prior to tumor segmentation, from image conversion and registration to brain extraction. Second, BraTS Segmentor enables orchestration of BraTS brain tumor segmentation algorithms for generation of fully-automated segmentations. Finally, Brats Fusionator can combine the resulting candidate segmentations into consensus segmentations using fusion methods such as majority voting and iterative SIMPLE fusion. The capabilities of our tools are illustrated with a practical example to enable easy translation to clinical and scientific practice.},
  langid = {english},
  pmcid = {PMC7201293},
  pmid = {32410929},
  keywords = {anonymization,brain extraction,brain tumor segmentation,BraTS,glioma,medical imaging,MRI data preprocessing},
  file = {/Users/scholz/Zotero/storage/SMTWAESI/Kofler et al. - 2020 - BraTS Toolkit Translating BraTS Brain Tumor Segme.pdf}
}

@inproceedings{linFocalLossDense2017,
  title = {Focal Loss for Dense Object Detection},
  booktitle = {Proceedings of the {{IEEE}} International Conference on Computer Vision},
  author = {Lin, Tsung-Yi and Goyal, Priya and Girshick, Ross and He, Kaiming and Doll{\'a}r, Piotr},
  year = {2017},
  pages = {2980--2988}
}

@inproceedings{loshchilovSGDRStochasticGradient2016,
  title = {{{SGDR}}: {{Stochastic Gradient Descent}} with {{Warm Restarts}}},
  booktitle = {International {{Conference}} on {{Learning Representations}}},
  author = {Loshchilov, Ilya and Hutter, Frank},
  year = {2016}
}

@article{louis2016WorldHealth2016,
  title = {The 2016 {{World Health Organization Classification}} of {{Tumors}} of the {{Central Nervous System}}: A Summary},
  shorttitle = {The 2016 {{World Health Organization Classification}} of {{Tumors}} of the {{Central Nervous System}}},
  author = {Louis, David N. and Perry, Arie and Reifenberger, Guido and {von Deimling}, Andreas and {Figarella-Branger}, Dominique and Cavenee, Webster K. and Ohgaki, Hiroko and Wiestler, Otmar D. and Kleihues, Paul and Ellison, David W.},
  year = {2016},
  month = jun,
  journal = {Acta Neuropathologica},
  volume = {131},
  number = {6},
  pages = {803--820},
  issn = {1432-0533},
  doi = {10.1007/s00401-016-1545-1},
  abstract = {The 2016 World Health Organization Classification of Tumors of the Central Nervous System is both a conceptual and practical advance over its 2007 predecessor. For the first time, the WHO classification of CNS tumors uses molecular parameters in addition to histology to define many tumor entities, thus formulating a concept for how CNS tumor diagnoses should be structured in the molecular era. As such, the 2016 CNS WHO presents major restructuring of the diffuse gliomas, medulloblastomas and other embryonal tumors, and incorporates new entities that are defined by both histology and molecular features, including glioblastoma, IDH-wildtype and glioblastoma, IDH-mutant; diffuse midline glioma, H3 K27M-mutant; RELA fusion-positive ependymoma; medulloblastoma, WNT-activated and medulloblastoma, SHH-activated; and embryonal tumour with multilayered rosettes, C19MC-altered. The 2016 edition has added newly recognized neoplasms, and has deleted some entities, variants and patterns that no longer have diagnostic and/or biological relevance. Other notable changes include the addition of brain invasion as a criterion for atypical meningioma and the introduction of a soft tissue-type grading system for the now combined entity of solitary fibrous tumor / hemangiopericytoma-a departure from the manner by which other CNS tumors are graded. Overall, it is hoped that the 2016 CNS WHO will facilitate clinical, experimental and epidemiological studies that will lead to improvements in the lives of patients with brain tumors.},
  langid = {english},
  pmid = {27157931},
  keywords = {Animals,Brain,Central Nervous System,Central Nervous System Neoplasms,Glioma,Humans,Meningioma,World Health Organization},
  file = {/Users/scholz/Zotero/storage/THECB2QX/Louis et al. - 2016 - The 2016 World Health Organization Classification .pdf}
}

@misc{martinabadiTensorFlowLargeScaleMachine2015,
  title = {{{TensorFlow}}: {{Large-Scale Machine Learning}} on {{Heterogeneous Systems}}},
  author = {{Mart{\'i}n Abadi} and {Ashish Agarwal} and {Paul Barham} and {Eugene Brevdo} and {Zhifeng Chen} and {Craig Citro} and {Greg S. Corrado} and {Andy Davis} and {Jeffrey Dean} and {Matthieu Devin} and {Sanjay Ghemawat} and {Ian Goodfellow} and {Andrew Harp} and {Geoffrey Irving} and {Michael Isard} and Jia, Yangqing and {Rafal Jozefowicz} and {Lukasz Kaiser} and {Manjunath Kudlur} and {Josh Levenberg} and {Dandelion Man{\'e}} and {Rajat Monga} and {Sherry Moore} and {Derek Murray} and {Chris Olah} and {Mike Schuster} and {Jonathon Shlens} and {Benoit Steiner} and {Ilya Sutskever} and {Kunal Talwar} and {Paul Tucker} and {Vincent Vanhoucke} and {Vijay Vasudevan} and {Fernanda Vi{\'e}gas} and {Oriol Vinyals} and {Pete Warden} and {Martin Wattenberg} and {Martin Wicke} and {Yuan Yu} and {Xiaoqiang Zheng}},
  year = {2015}
}

@article{matthewsComparisonPredictedObserved1975,
  title = {Comparison of the Predicted and Observed Secondary Structure of {{T4}} Phage Lysozyme},
  author = {Matthews, B. W.},
  year = {1975},
  month = oct,
  journal = {Biochimica et Biophysica Acta (BBA) - Protein Structure},
  volume = {405},
  number = {2},
  pages = {442--451},
  issn = {0005-2795},
  doi = {10.1016/0005-2795(75)90109-9},
  urldate = {2023-11-03},
  abstract = {Predictions of the secondary structure of T4 phage lysozyme, made by a number of investigators on the basis of the amino acid sequence, are compared with the structure of the protein determined experimentally by X-ray crystallography. Within the amino terminal half of the molecule the locations of helices predicted by a number of methods agree moderately well with the observed structure, however within the carboxyl half of the molecule the overall agreement is poor. For eleven different helix predictions, the coefficients giving the correlation between prediction and observation range from 0.14 to 0.42. The accuracy of the predictions for both {$\beta$}-sheet regions and for turns are generally lower than for the helices, and in a number of instances the agreement between prediction and observation is no better than would be expected for a random selection of residues. The structural predictions for T4 phage lysozyme are much less successful than was the case for adenylate kinase (Schulz et al. (1974) Nature 250, 140{\textendash}142). No one method of prediction is clearly superior to all others, and although empirical predictions based on larger numbers of known protein structure tend to be more accurate than those based on a limited sample, the improvement in accuracy is not dramatic, suggesting that the accuracy of current empirical predictive methods will not be substantially increased simply by the inclusion of more data from additional protein structure determinations.},
  file = {/Users/scholz/Zotero/storage/2IEZZNKX/0005279575901099.html}
}

@article{mazurowskiTrainingNeuralNetwork2008,
  title = {Training Neural Network Classifiers for Medical Decision Making: {{The}} Effects of Imbalanced Datasets on Classification Performance},
  shorttitle = {Training Neural Network Classifiers for Medical Decision Making},
  author = {Mazurowski, Maciej A. and Habas, Piotr A. and Zurada, Jacek M. and Lo, Joseph Y. and Baker, Jay A. and Tourassi, Georgia D.},
  year = {2008},
  month = mar,
  journal = {Neural Networks},
  series = {Advances in {{Neural Networks Research}}: {{IJCNN}} '07},
  volume = {21},
  number = {2},
  pages = {427--436},
  issn = {0893-6080},
  doi = {10.1016/j.neunet.2007.12.031},
  urldate = {2023-11-05},
  abstract = {This study investigates the effect of class imbalance in training data when developing neural network classifiers for computer-aided medical diagnosis. The investigation is performed in the presence of other characteristics that are typical among medical data, namely small training sample size, large number of features, and correlations between features. Two methods of neural network training are explored: classical backpropagation (BP) and particle swarm optimization (PSO) with clinically relevant training criteria. An experimental study is performed using simulated data and the conclusions are further validated on real clinical data for breast cancer diagnosis. The results show that classifier performance deteriorates with even modest class imbalance in the training data. Further, it is shown that BP is generally preferable over PSO for imbalanced training data especially with small data sample and large number of features. Finally, it is shown that there is no clear preference between oversampling and no compensation approach and some guidance is provided regarding a proper selection.},
  keywords = {Class imbalance,Classification,Computer-aided diagnosis,Feed-forward neural networks},
  file = {/Users/scholz/Zotero/storage/PDEADZVZ/Mazurowski et al. - 2008 - Training neural network classifiers for medical de.pdf;/Users/scholz/Zotero/storage/XC7L45D5/S0893608007002407.html}
}

@article{tahaMetricsEvaluating3D2015,
  title = {Metrics for Evaluating {{3D}} Medical Image Segmentation: Analysis, Selection, and Tool.},
  author = {Taha, Abdel Aziz and Hanbury, Allan},
  year = {2015},
  month = aug,
  journal = {BMC medical imaging},
  volume = {15},
  pages = {29},
  address = {{England}},
  issn = {1471-2342},
  doi = {10.1186/s12880-015-0068-x},
  abstract = {BACKGROUND: Medical Image segmentation is an important image processing step. Comparing images to evaluate the quality of segmentation is an essential part of  measuring progress in this research area. Some of the challenges in evaluating  medical segmentation are: metric selection, the use in the literature of multiple  definitions for certain metrics, inefficiency of the metric calculation  implementations leading to difficulties with large volumes, and lack of support  for fuzzy segmentation by existing metrics. RESULT: First we present an overview  of 20 evaluation metrics selected based on a comprehensive literature review. For  fuzzy segmentation, which shows the level of membership of each voxel to multiple  classes, fuzzy definitions of all metrics are provided. We present a discussion  about metric properties to provide a guide for selecting evaluation metrics.  Finally, we propose an efficient evaluation tool implementing the 20 selected  metrics. The tool is optimized to perform efficiently in terms of speed and  required memory, also if the image size is extremely large as in the case of  whole body MRI or CT volume segmentation. An implementation of this tool is  available as an open source project. CONCLUSION: We propose an efficient  evaluation tool for 3D medical image segmentation using 20 evaluation metrics and  provide guidelines for selecting a subset of these metrics that is suitable for  the data and the segmentation task.},
  langid = {english},
  pmcid = {PMC4533825},
  pmid = {26263899},
  keywords = {Algorithms,Brain Neoplasms/*pathology,Cone-Beam Computed Tomography/methods,Fuzzy Logic,Humans,{Image Processing, Computer-Assisted/*methods},{Imaging, Three-Dimensional/*methods},Magnetic Resonance Imaging/methods}
}

@article{vandermaatenVisualizingDataUsing2008,
  title = {Visualizing Data Using T-{{SNE}}.},
  author = {{Van der Maaten}, Laurens and Hinton, Geoffrey},
  year = {2008},
  journal = {Journal of machine learning research},
  volume = {9},
  number = {11}
}

@article{vandervoortErasmusGliomaDatabase2021,
  title = {The {{Erasmus Glioma Database}} ({{EGD}}): {{Structural MRI}} Scans, {{WHO}} 2016 Subtypes, and Segmentations of 774 Patients with Glioma},
  shorttitle = {The {{Erasmus Glioma Database}} ({{EGD}})},
  author = {{van der Voort}, Sebastian R. and Incekara, Fatih and Wijnenga, Maarten M. J. and Kapsas, Georgios and Gahrmann, Renske and Schouten, Joost W. and Dubbink, Hendrikus J. and Vincent, Arnaud J. P. E. and {van den Bent}, Martin J. and French, Pim J. and Klein, Stefan and Smits, Marion},
  year = {2021},
  month = aug,
  journal = {Data in Brief},
  volume = {37},
  pages = {107191},
  issn = {2352-3409},
  doi = {10.1016/j.dib.2021.107191},
  urldate = {2023-11-03},
  abstract = {The Erasmus Glioma Database (EGD) contains structural magnetic resonance imaging (MRI) scans, genetic and histological features (specifying the WHO 2016 subtype), and whole tumor segmentations of patients with glioma. Pre-operative MRI data of 774 patients with glioma (281 female, 492 male, 1 unknown, age range 19{\textendash}86 years) treated at the Erasmus MC between 2008 and 2018 is available. For all patients a pre-contrast T1-weighted, post-contrast T1-weighted, T2-weighted, and T2-weighted FLAIR scan are available, made on a variety of scanners from four different vendors. All scans are registered to a common atlas and defaced. Genetic and histological data consists of the IDH mutation status (available for 467 patients), 1p/19q co-deletion status (available for 259 patients), and grade (available for 716 patients). The full WHO 2016 subtype is available for 415 patients. Manual segmentations are available for 374 patients and automatically generated segmentations are available for 400 patients. The dataset can be used to relate the visual appearance of the tumor on the scan with the genetic and histological features, and to develop automatic segmentation methods.},
  keywords = {Brain,Genetics,Glioma,Magnetic resonance imaging,Radiomics,Segmentation,WHO 2016},
  file = {/Users/scholz/Zotero/storage/CL56DUWT/van der Voort et al. - 2021 - The Erasmus Glioma Database (EGD) Structural MRI .pdf;/Users/scholz/Zotero/storage/5XCNY38J/S2352340921004753.html}
}

@article{vanderwaltScikitimageImageProcessing2014,
  title = {Scikit-Image: Image Processing in {{Python}}},
  author = {{van der Walt}, St{\'e}fan and Sch{\"o}nberger, Johannes L. and {Nunez-Iglesias}, Juan and Boulogne, Fran{\c c}ois and Warner, Joshua D. and Yager, Neil and Gouillart, Emmanuelle and Yu, Tony and scikit-image {contributors}, the},
  year = {2014},
  month = jun,
  journal = {PeerJ},
  volume = {2},
  pages = {e453},
  issn = {2167-8359},
  doi = {10.7717/peerj.453},
  abstract = {scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal Modified BSD open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators. In this paper we highlight the advantages of open source to achieve the goals of the scikit-image library, and we showcase several real-world image processing applications that use scikit-image. More information can be found on the project homepage, http://scikit-image.org.},
  keywords = {Education,Image processing,Open source,Python,Reproducible research,Scientific programming,Visualization}
}

@article{virtanenSciPyFundamentalAlgorithms2020,
  title = {{{SciPy}} 1.0: {{Fundamental Algorithms}} for {{Scientific Computing}} in {{Python}}},
  author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and {van der Walt}, St{\'e}fan J. and Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and Kern, Robert and Larson, Eric and Carey, C J and Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and VanderPlas, Jake and Laxalde, Denis and Perktold, Josef and Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and Harris, Charles R. and Archibald, Anne M. and Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  year = {2020},
  journal = {Nature Methods},
  volume = {17},
  pages = {261--272},
  doi = {10.1038/s41592-019-0686-2}
}

@article{wen2021WHOClassification2021,
  title = {The 2021 {{WHO Classification}} of {{Tumors}} of the {{Central Nervous System}}: Clinical Implications},
  shorttitle = {The 2021 {{WHO Classification}} of {{Tumors}} of the {{Central Nervous System}}},
  author = {Wen, Patrick Y. and Packer, Roger J.},
  year = {2021},
  month = aug,
  journal = {Neuro-Oncology},
  volume = {23},
  number = {8},
  pages = {1215},
  publisher = {{Oxford University Press}},
  doi = {10.1093/neuonc/noab120},
  urldate = {2023-11-03},
  langid = {english},
  pmid = {34185090},
  file = {/Users/scholz/Zotero/storage/9K2PA5MT/Wen and Packer - 2021 - The 2021 WHO Classification of Tumors of the Centr.pdf}
}

@article{zhengOversamplingMethodImbalanced2015,
  title = {Oversampling Method for Imbalanced Classification},
  author = {Zheng, Zhuoyuan and Cai, Yunpeng and Li, Ye},
  year = {2015},
  journal = {Computing and Informatics},
  volume = {34},
  number = {5},
  pages = {1017--1037}
}


@article{bhattacharyaDeepLearningMedical2021,
  title = {Deep Learning and Medical Image Processing for Coronavirus ({{COVID-19}}) Pandemic: {{A}} Survey},
  shorttitle = {Deep Learning and Medical Image Processing for Coronavirus ({{COVID-19}}) Pandemic},
  author = {Bhattacharya, Sweta and Reddy Maddikunta, Praveen Kumar and Pham, Quoc-Viet and Gadekallu, Thippa Reddy and Krishnan S, Siva Rama and Chowdhary, Chiranji Lal and Alazab, Mamoun and Jalil Piran, {\relax Md}.},
  year = {2021},
  month = feb,
  journal = {Sustainable Cities and Society},
  volume = {65},
  pages = {102589},
  issn = {2210-6707},
  doi = {10.1016/j.scs.2020.102589},
  urldate = {2024-01-27},
  abstract = {Since December 2019, the coronavirus disease (COVID-19) outbreak has caused many death cases and affected all sectors of human life. With gradual progression of time, COVID-19 was declared by the world health organization (WHO) as an outbreak, which has imposed a heavy burden on almost all countries, especially ones with weaker health systems and ones with slow responses. In the field of healthcare, deep learning has been implemented in many applications, e.g., diabetic retinopathy detection, lung nodule classification, fetal localization, and thyroid diagnosis. Numerous sources of medical images (e.g., X-ray, CT, and MRI) make deep learning a great technique to combat the COVID-19 outbreak. Motivated by this fact, a large number of research works have been proposed and developed for the initial months of 2020. In this paper, we first focus on summarizing the state-of-the-art research works related to deep learning applications for COVID-19 medical image processing. Then, we provide an overview of deep learning and its applications to healthcare found in the last decade. Next, three use cases in China, Korea, and Canada are also presented to show deep learning applications for COVID-19 medical image processing. Finally, we discuss several challenges and issues related to deep learning implementations for COVID-19 medical image processing, which are expected to drive further studies in controlling the outbreak and controlling the crisis, which results in smart healthy cities.},
  keywords = {Artificial intelligence (AI),Big data,Coronavirus pandemic,COVID-19,Deep learning,Epidemic outbreak,Medical image processing},
  file = {/Users/scholz/Zotero/storage/2EDMP93T/Bhattacharya et al. - 2021 - Deep learning and medical image processing for cor.pdf;/Users/scholz/Zotero/storage/BGHXRZL8/S2210670720308076.html}
}

@article{meiserDistinctStimulatoryCDC12023,
  title = {A Distinct Stimulatory {{cDC1}} Subpopulation Amplifies {{CD8}}+ {{T}} Cell Responses in Tumors for Protective Anti-Cancer Immunity},
  author = {Meiser, Philippa and Knolle, Moritz A. and Hirschberger, Anna and De Almeida, Gustavo P. and Bayerl, Felix and Lacher, Sebastian and Pedde, Anna-Marie and Flommersfeld, Sophie and H{\"o}nninger, Julian and Stark, Leonhard and St{\"o}gbauer, Fabian and Anton, Martina and Wirth, Markus and Wohlleber, Dirk and Steiger, Katja and Buchholz, Veit R. and Wollenberg, Barbara and Zielinski, Christina E. and Braren, Rickmer and Rueckert, Daniel and Knolle, Percy A. and Kaissis, Georgios and B{\"o}ttcher, Jan P.},
  year = {2023},
  month = aug,
  journal = {Cancer Cell},
  volume = {41},
  number = {8},
  pages = {1498-1515.e10},
  issn = {15356108},
  doi = {10.1016/j.ccell.2023.06.008},
  urldate = {2024-01-27},
  abstract = {Type 1 conventional dendritic cells (cDC1) can support T cell responses within tumors but whether this determines protective versus ineffective anti-cancer immunity is poorly understood. Here, we use imagingbased deep learning to identify intratumoral cDC1-CD8+ T cell clustering as a unique feature of protective anti-cancer immunity. These clusters form selectively in stromal tumor regions and constitute niches in which cDC1 activate TCF1+ stem-like CD8+ T cells. We identify a distinct population of immunostimulatory CCR7neg cDC1 that produce CXCL9 to promote cluster formation and cross-present tumor antigens within these niches, which is required for intratumoral CD8+ T cell differentiation and expansion and promotes cancer immune control. Similarly, in human cancers, CCR7neg cDC1 interact with CD8+ T cells in clusters and are associated with patient survival. Our findings reveal an intratumoral phase of the anti-cancer T cell response orchestrated by tumor-residing cDC1 that determines protective versus ineffective immunity and could be exploited for cancer therapy.},
  langid = {english},
  file = {/Users/scholz/Zotero/storage/AX7X4QGV/Meiser et al. - 2023 - A distinct stimulatory cDC1 subpopulation amplifie.pdf}
}



@inproceedings{goodfellowGenerativeAdversarialNets2014a,
  title = {Generative {{Adversarial Nets}}},
  booktitle = {Advances in {{Neural Information Processing Systems}}},
  author = {Goodfellow, Ian and {Pouget-Abadie}, Jean and Mirza, Mehdi and Xu, Bing and {Warde-Farley}, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
  year = {2014},
  volume = {27},
  publisher = {{Curran Associates, Inc.}},
  urldate = {2024-01-27},
  abstract = {We propose a new framework for estimating generative models via adversarial nets, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to 1/2 everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitatively evaluation of the generated samples.},
  file = {/Users/scholz/Zotero/storage/G33R74P7/Goodfellow et al. - 2014 - Generative Adversarial Nets.pdf}
}

@article{liEIDGANGenerativeAdversarial2023,
  title = {{{EID-GAN}}: {{Generative Adversarial Nets}} for {{Extremely Imbalanced Data Augmentation}}},
  shorttitle = {{{EID-GAN}}},
  author = {Li, Wei and Chen, Jinlin and Cao, Jiannong and Ma, Chao and Wang, Jia and Cui, Xiaohui and Chen, Ping},
  year = {2023},
  month = mar,
  journal = {IEEE Transactions on Industrial Informatics},
  volume = {19},
  number = {3},
  pages = {3208--3218},
  issn = {1941-0050},
  doi = {10.1109/TII.2022.3182781},
  urldate = {2024-01-27},
  abstract = {Imbalanced data cause deep neural networks to output biased results, and it becomes more serious when facing extremely imbalanced data regarding the outliers with tiny size (the ratio of the outlier size to the image size is around 0.05\%). Many data argumentation models are proposed to supplement imbalanced data to alleviate biased results. However, the existing augmentation models cannot synthesize tiny outliers, which make the generated data unavailable. In this article, we propose a new augmentation model named extremely imbalanced data augmentation generative adversarial nets (EID-GANs) to address the extremely imbalanced data augmentation problem. First, we design a new penalty function by subtracting the outliers from the cropped region of generated instance to guide the generator to learn the features of outliers. After this, we combine the output value of the penalty function with the generator loss to jointly update the generator's parameters with backpropagation. Second, we propose a new evaluation approach that adopts two outlier detectors with k-fold cross-validation to assess the availability of generated instances. We conduct extensive experiments to demonstrate the significant performance improvement of EID-GAN on two extremely imbalanced datasets, which are the industrial Piston and the Fabric datasets, and one general imbalanced dataset, i.e., the public DAGM dataset. The experimental results show that our EID-GAN outperforms the state-of-the-art (SOTA) augmentation models on different imbalanced datasets.},
  keywords = {Data models,Detectors,Extremely imbalanced data augmentation,Fabrics,generated data evaluation,generative adversarial net (GAN),Generators,norm penalty function,Pistons,Prototypes,Training},
  file = {/Users/scholz/Zotero/storage/ZSQGP358/9795891.html}
}

@inproceedings{qasimRedGANAttackingClass2020,
  title = {Red-{{GAN}}: {{Attacking}} Class Imbalance via Conditioned Generation. {{Yet}} Another Medical Imaging Perspective.},
  shorttitle = {Red-{{GAN}}},
  booktitle = {Proceedings of the {{Third Conference}} on {{Medical Imaging}} with {{Deep Learning}}},
  author = {Qasim, Ahmad B. and Ezhov, Ivan and Shit, Suprosanna and Schoppe, Oliver and Paetzold, Johannes C. and Sekuboyina, Anjany and Kofler, Florian and Lipkova, Jana and Li, Hongwei and Menze, Bjoern},
  year = {2020},
  month = sep,
  pages = {655--668},
  publisher = {{PMLR}},
  issn = {2640-3498},
  urldate = {2024-01-27},
  abstract = {Exploiting learning algorithms under scarce data regimes is a limitation and a reality of the medical imaging field. In an attempt to mitigate the problem, we propose a data augmentation protocol based on generative adversarial networks. We condition the networks at a pixel-level (segmentation mask) and at a global-level information (acquisition environment or lesion type). Such conditioning provides immediate access to the image-label pairs while controlling global class specific appearance of the synthesized images. To stimulate synthesis of the features relevant for the segmentation task, an additional passive player in a form of segmentor is introduced into the the adversarial game. We validate the approach on two medical datasets: BraTS, ISIC. By controlling the class distribution through injection of synthetic images into the training set we achieve control over the accuracy levels of the datasets' classes. The code is available at https://github.com/IvanEz/Red-GAN.},
  langid = {english},
  file = {/Users/scholz/Zotero/storage/B4KVK9XS/Qasim et al. - 2020 - Red-GAN Attacking class imbalance via conditioned.pdf}
}

@inproceedings{hoDenoisingDiffusionProbabilistic2020a,
  title = {Denoising {{Diffusion Probabilistic Models}}},
  booktitle = {Advances in {{Neural Information Processing Systems}}},
  author = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
  year = {2020},
  volume = {33},
  pages = {6840--6851},
  publisher = {{Curran Associates, Inc.}},
  urldate = {2024-01-27},
  abstract = {We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.},
  file = {/Users/scholz/Zotero/storage/AL9WMGB7/Ho et al. - 2020 - Denoising Diffusion Probabilistic Models.pdf}
}

@inproceedings{qinClassBalancingDiffusionModels2023,
  title = {Class-{{Balancing Diffusion Models}}},
  booktitle = {2023 {{IEEE}}/{{CVF Conference}} on {{Computer Vision}} and {{Pattern Recognition}} ({{CVPR}})},
  author = {Qin, Yiming and Zheng, Huangjie and Yao, Jiangchao and Zhou, Mingyuan and Zhang, Ya},
  year = {2023},
  month = jun,
  pages = {18434--18443},
  publisher = {{IEEE}},
  address = {{Vancouver, BC, Canada}},
  doi = {10.1109/CVPR52729.2023.01768},
  urldate = {2024-01-27},
  abstract = {Diffusion-based models have shown the merits of generating high-quality visual data while preserving better diversity in recent studies. However, such observation is only justified with curated data distribution, where the data samples are nicely pre-processed to be uniformly distributed in terms of their labels. In practice, a long-tailed data distribution appears more common and how diffusion models perform on such class-imbalanced data remains unknown. In this work, we first investigate this problem and observe significant degradation in both diversity and fidelity when the diffusion model is trained on datasets with class-imbalanced distributions. Especially in tail classes, the generations largely lose diversity and we observe severe mode-collapse issues. To tackle this problem, we set from the hypothesis that the data distribution is not class-balanced, and propose Class-Balancing Diffusion Models (CBDM) that are trained with a distribution adjustment regularizer as a solution. Experiments show that images generated by CBDM exhibit higher diversity and quality in both quantitative and qualitative ways. Our method benchmarked the generation results on CIFAR100/CIFAR100LT dataset and shows outstanding performance on the downstream recognition task.},
  isbn = {9798350301298},
  langid = {english},
  file = {/Users/scholz/Zotero/storage/RTDVVDGH/Qin et al. - 2023 - Class-Balancing Diffusion Models.pdf}
}


@inproceedings{dhariwalDiffusionModelsBeat2021a,
  title = {Diffusion {{Models Beat GANs}} on {{Image Synthesis}}},
  booktitle = {Advances in {{Neural Information Processing Systems}}},
  author = {Dhariwal, Prafulla and Nichol, Alexander},
  year = {2021},
  volume = {34},
  pages = {8780--8794},
  publisher = {{Curran Associates, Inc.}},
  urldate = {2024-01-27},
  file = {/Users/scholz/Zotero/storage/NCZ9I7FZ/Dhariwal and Nichol - 2021 - Diffusion Models Beat GANs on Image Synthesis.pdf}
}


@article{chawlaSMOTESyntheticMinority2002,
  title = {{{SMOTE}}: {{Synthetic Minority Over-sampling Technique}}},
  shorttitle = {{{SMOTE}}},
  author = {Chawla, N. V. and Bowyer, K. W. and Hall, L. O. and Kegelmeyer, W. P.},
  year = {2002},
  month = jun,
  journal = {Journal of Artificial Intelligence Research},
  volume = {16},
  pages = {321--357},
  issn = {1076-9757},
  doi = {10.1613/jair.953},
  urldate = {2024-01-27},
  abstract = {An approach to the construction of classifiers from    imbalanced datasets is described. A dataset is imbalanced if the    classification categories are not approximately equally    represented. Often real-world data sets are predominately composed of    ``normal'' examples with only a small percentage of ``abnormal'' or    ``interesting'' examples. It is also the case that the cost of    misclassifying an abnormal (interesting) example as a normal example    is often much higher than the cost of the reverse    error. Under-sampling of the majority (normal) class has been proposed    as a good means of increasing the sensitivity of a classifier to the    minority class. This paper shows that a combination of our method of    over-sampling the minority (abnormal) class and under-sampling the    majority (normal) class can achieve better classifier performance (in    ROC space) than only under-sampling the majority class.  This paper    also shows that a combination of our method of over-sampling the    minority class and under-sampling the majority class can achieve    better classifier performance (in ROC space) than varying the loss    ratios in Ripper or class priors in Naive Bayes. Our method of    over-sampling the minority class involves creating synthetic minority    class examples.  Experiments are performed using C4.5, Ripper and a    Naive Bayes classifier. The method is evaluated using the area under    the Receiver Operating Characteristic curve (AUC) and the ROC convex    hull strategy.},
  copyright = {Copyright (c)},
  langid = {english},
  file = {/Users/scholz/Zotero/storage/FQJ5R8R3/Chawla et al. - 2002 - SMOTE Synthetic Minority Over-sampling Technique.pdf}
}



@article{cindil2022prediction,
  title = {Prediction of {{IDH}} Mutation Status in High-Grade Gliomas Using {{DWI}} and High {{T1-weight DSC-MRI}}},
  author = {Cindil, Emetullah and Sendur, Halit Nahit and Cerit, Mahi Nur and Erdogan, Nesrin and Celebi, Filiz and Dag, Nurullah and Celtikci, Emrah and Inan, Arda and Oner, Yusuf and Tali, Turgut},
  year = {2022},
  journal = {Academic radiology},
  volume = {29},
  pages = {S52--S62},
  publisher = {{Elsevier}}
}

@article{van2019predicting,
  title = {Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm},
  author = {{van der Voort}, Sebastian R and Incekara, Fatih and Wijnenga, Maarten MJ and Kapas, Georgios and Gardeniers, Mayke and Schouten, Joost W and Starmans, Martijn PA and Nandoe Tewarie, Rishie and Lycklama, Geert J and French, Pim J and others},
  year = {2019},
  journal = {Clinical Cancer Research},
  volume = {25},
  number = {24},
  pages = {7455--7462},
  publisher = {{AACR}}
}


@article{briaAddressingClassImbalance2020,
  title = {Addressing Class Imbalance in Deep Learning for Small Lesion Detection on Medical Images},
  author = {Bria, Alessandro and Marrocco, Claudio and Tortorella, Francesco},
  year = {2020},
  month = may,
  journal = {Computers in Biology and Medicine},
  volume = {120},
  pages = {103735},
  issn = {0010-4825},
  doi = {10.1016/j.compbiomed.2020.103735},
  urldate = {2024-01-28},
  abstract = {Deep learning methods utilizing Convolutional Neural Networks (CNNs) have led to dramatic advances in automated understanding of medical images. However, in many medical image classification tasks, lesions occupy only a few pixels of the image. This results in a significant class imbalance between lesion and background. From recent literature, it is known that class imbalance may negatively affect the performance of CNN classification. However, very few research exists in the context of lesion detection. In this work, we propose a two-stage deep learning framework able to deal with the high class imbalance encountered during training of small lesion detectors. First, we train a deep cascade (DC) of long sequences of decision trees with an algorithm designed to handle unbalanced data that also drastically reduces the number of background samples reaching the final stage. The remaining samples are fed to a CNN, whose training benefits from both rebalance and hard mining done by the DC. We evaluated DC-CNN on two severely unbalanced classification problems: microcalcification detection and microaneurysm detection. In both cases, DC-CNN outperformed the CNNs trained with commonly used methods for addressing class imbalance such as oversampling, undersampling, hard mining, cost sensitive learning, and one-class classification. The DC-CNN was also\,{$\sim$} 10x faster than CNN at test time.},
  keywords = {Class imbalance,Deep learning,Lesion detection,Microaneurysms,Microcalcifications},
  file = {/Users/scholz/Zotero/storage/FLRBSDCW/S0010482520301177.html}
}

@article{gaoHandlingImbalancedMedical2020,
  title = {Handling Imbalanced Medical Image Data: {{A}} Deep-Learning-Based One-Class Classification Approach},
  shorttitle = {Handling Imbalanced Medical Image Data},
  author = {Gao, Long and Zhang, Lei and Liu, Chang and Wu, Shandong},
  year = {2020},
  month = aug,
  journal = {Artificial Intelligence in Medicine},
  volume = {108},
  pages = {101935},
  issn = {0933-3657},
  doi = {10.1016/j.artmed.2020.101935},
  urldate = {2024-01-28},
  abstract = {In clinical settings, a lot of medical image datasets suffer from the imbalance problem which hampers the detection of outliers (rare health care events), as most classification methods assume an equal occurrence of classes. In this way, identifying outliers in imbalanced datasets has become a crucial issue. To help address this challenge, one-class classification, which focuses on learning a model using samples from only a single given class, has attracted increasing attention. Previous one-class modeling usually uses feature mapping or feature fitting to enforce the feature learning process. However, these methods are limited for medical images which usually have complex features. In this paper, a novel method is proposed to enable deep learning models to optimally learn single-class-relevant inherent imaging features by leveraging the concept of imaging complexity. We investigate and compare the effects of simple but effective perturbing operations applied to images to capture imaging complexity and to enhance feature learning. Extensive experiments are performed on four clinical datasets to show that the proposed method outperforms four state-of-the-art methods.},
  keywords = {Data imbalance,Deep learning,Image complexity,Medical image classification},
  file = {/Users/scholz/Zotero/storage/X4GVC792/Gao et al. - 2020 - Handling imbalanced medical image data A deep-lea.pdf;/Users/scholz/Zotero/storage/Q7WTD5WB/S093336572030261X.html}
}

@article{liAnalyzingOverfittingClass2021,
  title = {Analyzing {{Overfitting Under Class Imbalance}} in {{Neural Networks}} for {{Image Segmentation}}},
  author = {Li, Zeju and Kamnitsas, Konstantinos and Glocker, Ben},
  year = {2021},
  month = mar,
  journal = {IEEE Transactions on Medical Imaging},
  volume = {40},
  number = {3},
  pages = {1065--1077},
  issn = {1558-254X},
  doi = {10.1109/TMI.2020.3046692},
  urldate = {2024-01-28},
  abstract = {Class imbalance poses a challenge for developing unbiased, accurate predictive models. In particular, in image segmentation neural networks may overfit to the foreground samples from small structures, which are often heavily under-represented in the training set, leading to poor generalization. In this study, we provide new insights on the problem of overfitting under class imbalance by inspecting the network behavior. We find empirically that when training with limited data and strong class imbalance, at test time the distribution of logit activations may shift across the decision boundary, while samples of the well-represented class seem unaffected. This bias leads to a systematic under-segmentation of small structures. This phenomenon is consistently observed for different databases, tasks and network architectures. To tackle this problem, we introduce new asymmetric variants of popular loss functions and regularization techniques including a large margin loss, focal loss, adversarial training, mixup and data augmentation, which are explicitly designed to counter logit shift of the under-represented classes. Extensive experiments are conducted on several challenging segmentation tasks. Our results demonstrate that the proposed modifications to the objective function can lead to significantly improved segmentation accuracy compared to baselines and alternative approaches.},
  keywords = {class imbalance,Data models,image segmentation,Image segmentation,Lesions,Overfitting,Sensitivity,Task analysis,Training,Training data},
  file = {/Users/scholz/Zotero/storage/AYXGYZSQ/Li et al. - 2021 - Analyzing Overfitting Under Class Imbalance in Neu.pdf}
}

@article{quAssessingMitigatingEffects2020,
  title = {Assessing and Mitigating the Effects of Class Imbalance in Machine Learning with Application to {{X-ray}} Imaging},
  author = {Qu, Wendi and Balki, Indranil and Mendez, Mauro and Valen, John and Levman, Jacob and Tyrrell, Pascal N.},
  year = {2020},
  month = dec,
  journal = {International Journal of Computer Assisted Radiology and Surgery},
  volume = {15},
  number = {12},
  pages = {2041--2048},
  issn = {1861-6429},
  doi = {10.1007/s11548-020-02260-6},
  urldate = {2024-01-28},
  abstract = {Machine learning (ML) algorithms are well known to exhibit variations in prediction accuracy when provided with imbalanced training sets typically seen in medical imaging (MI) due to the imbalanced ratio of pathological and normal cases. This paper presents a thorough investigation of the effects of class imbalance and methods for mitigating class imbalance in ML algorithms applied to MI.},
  langid = {english},
  keywords = {Class imbalance,Machine learning,Medical imaging,Radiology,X-ray}
}



@article{jaffariNovelFocalPhi2021,
  title = {A Novel Focal Phi Loss for Power Line Segmentation with Auxiliary Classifier {{U-Net}}},
  author = {Jaffari, Rabeea and Hashmani, Manzoor Ahmed and {Reyes-Aldasoro}, Constantino Carlos},
  year = {2021},
  journal = {Sensors},
  volume = {21},
  number = {8},
  pages = {2803},
  publisher = {{MDPI}}
}



@article{ahmadDeepLearningBreakthrough2020,
  title = {Deep {{Learning}}: {{A Breakthrough}} in {{Medical Imaging}}},
  shorttitle = {Deep {{Learning}}},
  author = {Ahmad, Hafiz Mughees and Khan, Muhammad Jaleed and Yousaf, Adeel and Ghuffar, Sajid and Khurshid, Khurram},
  year = {2020},
  journal = {Current Medical Imaging},
  volume = {16},
  number = {8},
  pages = {946--956},
  issn = {1573-4056},
  doi = {10.2174/1573405615666191219100824},
  abstract = {Deep learning has attracted great attention in the medical imaging community as a promising solution for automated, fast and accurate medical image analysis, which is mandatory for quality healthcare. Convolutional neural networks and its variants have become the most preferred and widely used deep learning models in medical image analysis. In this paper, concise overviews of the modern deep learning models applied in medical image analysis are provided and the key tasks performed by deep learning models, i.e. classification, segmentation, retrieval, detection, and registration are reviewed in detail. Some recent researches have shown that deep learning models can outperform medical experts in certain tasks. With the significant breakthroughs made by deep learning methods, it is expected that patients will soon be able to safely and conveniently interact with AI-based medical systems and such intelligent systems will actually improve patient healthcare. There are various complexities and challenges involved in deep learning-based medical image analysis, such as limited datasets. But researchers are actively working in this area to mitigate these challenges and further improve health care with AI.},
  langid = {english},
  pmid = {33081657},
  keywords = {Classification,deep learning,Deep Learning,detection,Diagnostic Imaging,Humans,{Image Processing, Computer-Assisted},medical image analysis,{Neural Networks, Computer},Radiography,registration,retrieval,segmentation}
}

@article{litjensSurveyDeepLearning2017,
  title = {A Survey on Deep Learning in Medical Image Analysis},
  author = {Litjens, Geert and Kooi, Thijs and Bejnordi, Babak Ehteshami and Setio, Arnaud Arindra Adiyoso and Ciompi, Francesco and Ghafoorian, Mohsen and {van der Laak}, Jeroen A. W. M. and {van Ginneken}, Bram and S{\'a}nchez, Clara I.},
  year = {2017},
  month = dec,
  journal = {Medical Image Analysis},
  volume = {42},
  pages = {60--88},
  issn = {1361-8423},
  doi = {10.1016/j.media.2017.07.005},
  abstract = {Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research.},
  langid = {english},
  pmid = {28778026},
  keywords = {Algorithms,Convolutional neural networks,Deep learning,Diagnostic Imaging,Humans,{Image Processing, Computer-Assisted},Machine Learning,Medical imaging,{Neural Networks, Computer},Survey},
  file = {/Users/scholz/Zotero/storage/VUISIQJZ/Litjens et al. - 2017 - A survey on deep learning in medical image analysi.pdf}
}

@article{orenArtificialIntelligenceMedical2020,
  title = {Artificial Intelligence in Medical Imaging: Switching from Radiographic Pathological Data to Clinically Meaningful Endpoints},
  shorttitle = {Artificial Intelligence in Medical Imaging},
  author = {Oren, Ohad and Gersh, Bernard J. and Bhatt, Deepak L.},
  year = {2020},
  month = sep,
  journal = {The Lancet Digital Health},
  volume = {2},
  number = {9},
  pages = {e486-e488},
  publisher = {{Elsevier}},
  issn = {2589-7500},
  doi = {10.1016/S2589-7500(20)30160-6},
  urldate = {2024-01-29},
  langid = {english},
  pmid = {33328116},
  file = {/Users/scholz/Zotero/storage/XRX5VZJQ/Oren et al. - 2020 - Artificial intelligence in medical imaging switch.pdf}
}

@article{pinto-coelhoHowArtificialIntelligence2023,
  title = {How {{Artificial Intelligence Is Shaping Medical Imaging Technology}}: {{A Survey}} of {{Innovations}} and {{Applications}}},
  shorttitle = {How {{Artificial Intelligence Is Shaping Medical Imaging Technology}}},
  author = {{Pinto-Coelho}, Lu{\'i}s},
  year = {2023},
  month = dec,
  journal = {Bioengineering},
  volume = {10},
  number = {12},
  pages = {1435},
  issn = {2306-5354},
  doi = {10.3390/bioengineering10121435},
  urldate = {2024-01-29},
  abstract = {The integration of artificial intelligence (AI) into medical imaging has guided in an era of transformation in healthcare. This literature review explores the latest innovations and applications of AI in the field, highlighting its profound impact on medical diagnosis and patient care. The innovation segment explores cutting-edge developments in AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, which have significantly improved the accuracy and efficiency of medical image analysis. These innovations have enabled rapid and accurate detection of abnormalities, from identifying tumors during radiological examinations to detecting early signs of eye disease in retinal images. The article also highlights various applications of AI in medical imaging, including radiology, pathology, cardiology, and more. AI-based diagnostic tools not only speed up the interpretation of complex images but also improve early detection of disease, ultimately delivering better outcomes for patients. Additionally, AI-based image processing facilitates personalized treatment plans, thereby optimizing healthcare delivery. This literature review highlights the paradigm shift that AI has brought to medical imaging, highlighting its role in revolutionizing diagnosis and patient care. By combining cutting-edge AI techniques and their practical applications, it is clear that AI will continue shaping the future of healthcare in profound and positive ways.},
  pmcid = {PMC10740686},
  pmid = {38136026},
  file = {/Users/scholz/Zotero/storage/96T93587/Pinto-Coelho - 2023 - How Artificial Intelligence Is Shaping Medical Ima.pdf}
}


@article{isensee2021nnu,
  title={nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation},
  author={Isensee, Fabian and Jaeger, Paul F and Kohl, Simon AA and Petersen, Jens and Maier-Hein, Klaus H},
  journal={Nature methods},
  volume={18},
  number={2},
  pages={203--211},
  year={2021},
  publisher={Nature Publishing Group}
}

@article{ahnDeepLearningModel2018,
  title = {A Deep Learning Model for the Detection of Both Advanced and Early Glaucoma Using Fundus Photography},
  author = {Ahn, Jin Mo and Kim, Sangsoo and Ahn, Kwang-Sung and Cho, Sung-Hoon and Lee, Kwan Bok and Kim, Ungsoo Samuel},
  year = {2018},
  month = nov,
  journal = {PLOS ONE},
  volume = {13},
  number = {11},
  pages = {e0207982},
  publisher = {{Public Library of Science}},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0207982},
  urldate = {2024-02-03},
  abstract = {Purpose To build a deep learning model to diagnose glaucoma using fundus photography. Design Cross sectional case study Subjects, Participants and Controls: A total of 1,542 photos (786 normal controls, 467 advanced glaucoma and 289 early glaucoma patients) were obtained by fundus photography. Method The whole dataset of 1,542 images were split into 754 training, 324 validation and 464 test datasets. These datasets were used to construct simple logistic classification and convolutional neural network using Tensorflow. The same datasets were used to fine tune pre-trained GoogleNet Inception v3 model. Results The simple logistic classification model showed a training accuracy of 82.9\%, validation accuracy of 79.9\% and test accuracy of 77.2\%. Convolutional neural network achieved accuracy and area under the receiver operating characteristic curve (AUROC) of 92.2\% and 0.98 on the training data, 88.6\% and 0.95 on the validation data, and 87.9\% and 0.94 on the test data. Transfer-learned GoogleNet Inception v3 model achieved accuracy and AUROC of 99.7\% and 0.99 on training data, 87.7\% and 0.95 on validation data, and 84.5\% and 0.93 on test data. Conclusion Both advanced and early glaucoma could be correctly detected via machine learning, using only fundus photographs. Our new model that is trained using convolutional neural network is more efficient for the diagnosis of early glaucoma than previously published models.},
  langid = {english},
  keywords = {Computer architecture,Deep learning,Glaucoma,Imaging techniques,Machine learning,Neural networks,Photography,Vision},
  file = {/Users/scholz/Zotero/storage/ND3PHSIW/Ahn et al. - 2018 - A deep learning model for the detection of both ad.pdf}
}


@article{budaSystematicStudyClass2018,
  title = {A Systematic Study of the Class Imbalance Problem in Convolutional Neural Networks},
  author = {Buda, Mateusz and Maki, Atsuto and Mazurowski, Maciej A.},
  year = {2018},
  month = oct,
  journal = {Neural Networks},
  volume = {106},
  pages = {249--259},
  issn = {0893-6080},
  doi = {10.1016/j.neunet.2018.07.011},
  urldate = {2024-02-04},
  abstract = {In this study, we systematically investigate the impact of class imbalance on classification performance of convolutional neural networks (CNNs) and compare frequently used methods to address the issue. Class imbalance is a common problem that has been comprehensively studied in classical machine learning, yet very limited systematic research is available in the context of deep learning. In our study, we use three benchmark datasets of increasing complexity, MNIST, CIFAR-10 and ImageNet, to investigate the effects of imbalance on classification and perform an extensive comparison of several methods to address the issue: oversampling, undersampling, two-phase training, and thresholding that compensates for prior class probabilities. Our main evaluation metric is area under the receiver operating characteristic curve (ROC AUC) adjusted to multi-class tasks since overall accuracy metric is associated with notable difficulties in the context of imbalanced data. Based on results from our experiments we conclude that (i)~the effect of class imbalance on classification performance is detrimental; (ii)~the method of addressing class imbalance that emerged as dominant in almost all analyzed scenarios was oversampling; (iii)~oversampling should be applied to the level that completely eliminates the imbalance, whereas the optimal undersampling ratio depends on the extent of imbalance; (iv)~as opposed to some classical machine learning models, oversampling does not cause overfitting of CNNs; (v)~thresholding should be applied to compensate for prior class probabilities when overall number of properly classified cases is of interest.},
  keywords = {Class imbalance,Convolutional neural networks,Deep learning,Image classification}
}


@article{sparck1972statistical,
  title={A statistical interpretation of term specificity and its application in retrieval},
  author={Sparck Jones, Karen},
  journal={Journal of documentation},
  volume={28},
  number={1},
  pages={11--21},
  year={1972},
  publisher={MCB UP Ltd}
}

@inproceedings{abhishek2021matthews,
  title={Matthews correlation coefficient loss for deep convolutional networks: Application to skin lesion segmentation},
  author={Abhishek, Kumar and Hamarneh, Ghassan},
  booktitle={2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)},
  pages={225--229},
  year={2021},
  organization={IEEE}
}