@article{wildeboer_artificial_2020,
	title = {Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods},
	volume = {189},
	issn = {0169-2607},
	url = {http://www.sciencedirect.com/science/article/pii/S0169260719310442},
	doi = {10.1016/j.cmpb.2020.105316},
	abstract = {Prostate cancer represents today the most typical example of a pathology whose diagnosis requires multiparametric imaging, a strategy where multiple imaging techniques are combined to reach an acceptable diagnostic performance. However, the reviewing, weighing and coupling of multiple images not only places additional burden on the radiologist, it also complicates the reviewing process. Prostate cancer imaging has therefore been an important target for the development of computer-aided diagnostic (CAD) tools. In this survey, we discuss the advances in CAD for prostate cancer over the last decades with special attention to the deep-learning techniques that have been designed in the last few years. Moreover, we elaborate and compare the methods employed to deliver the CAD output to the operator for further medical decision making.},
	language = {en},
	urldate = {2020-01-15},
	journal = {Computer Methods and Programs in Biomedicine},
	author = {Wildeboer, Rogier R. and van Sloun, Ruud J. G. and Wijkstra, Hessel and Mischi, Massimo},
	month = jun,
	year = {2020},
	keywords = {Machine learning, Prostate cancer, Magnetic resonance imaging, Ultrasound, Artificial intelligence, Computer-aided detection, Computer-aided diagnosis, Multiparametric imaging},
	pages = {105316},
	file = {ScienceDirect Full Text PDF:/home/duran/Zotero/storage/92H53RXL/Wildeboer et al. - 2020 - Artificial intelligence in multiparametric prostat.pdf:application/pdf;ScienceDirect Snapshot:/home/duran/Zotero/storage/WG5ADE7G/S0169260719310442.html:text/html}
}

@article{ploussard_contemporary_2011,
	title = {The {Contemporary} {Concept} of {Significant} {Versus} {Insignificant} {Prostate} {Cancer}},
	volume = {60},
	issn = {0302-2838, 1873-7560},
	url = {https://www.europeanurology.com/article/S0302-2838(11)00495-7/abstract},
	doi = {10.1016/j.eururo.2011.05.006},
	abstract = {{\textless}h2{\textgreater}Abstract{\textless}/h2{\textgreater}{\textless}h3{\textgreater}Context{\textless}/h3{\textgreater}{\textless}p{\textgreater}The notion of insignificant prostate cancer (Ins-PCa) has progressively emerged in the past two decades. The clinical relevance of such a definition was based on the fact that low-grade, small-volume, and organ-confined prostate cancer (PCa) may be indolent and unlikely to progress to biologic significance in the absence of treatment.{\textless}/p{\textgreater}{\textless}h3{\textgreater}Objective{\textless}/h3{\textgreater}{\textless}p{\textgreater}To review the definition of Ins-PCa, its incidence, and the clinical impact of Ins-PCa on the contemporary management of PCa.{\textless}/p{\textgreater}{\textless}h3{\textgreater}Evidence acquisition{\textless}/h3{\textgreater}{\textless}p{\textgreater}A review of the literature was performed using the Medline, Scopus, and Web of Science databases with no restriction on language up to September 2010. The literature search used the following terms: \textit{insignificant, indolent, minute, microfocal, minimal, low volume, low risk,} and \textit{prostate cancer.}{\textless}/p{\textgreater}{\textless}h3{\textgreater}Evidence synthesis{\textless}/h3{\textgreater}{\textless}p{\textgreater}The most commonly used criteria to define Ins-PCa are based on the pathologic assessment of the radical prostatectomy specimen: (1) Gleason score ≤6 without Gleason pattern 4 or 5, (2) organ-confined disease, and (3) tumour volume{\textless}0.5cm$^{\textrm{3}}$. Several preoperative criteria and prognostication tools for predicting Ins-PCa have been suggested. Nomograms are best placed to estimate the risk of progression on an individualised basis, but a substantial proportion of men with a high probability of harbouring Ins-PCa are at risk for pathologic understaging and/or undergrading. Thus, there is an ongoing need for identifying novel and more accurate predictors of Ins-PCa to improve the distinction between insignificant versus significant disease and thus to promote the adequate management of PCa patients at low risk for progression.{\textless}/p{\textgreater}{\textless}h3{\textgreater}Conclusions{\textless}/h3{\textgreater}{\textless}p{\textgreater}The exciting challenge of obtaining the pretreatment diagnostic tools that can really distinguish insignificant from significant PCa should be one of the main objectives of urologists in the following years to decrease the risk of overtreatment of Ins-PCa.{\textless}/p{\textgreater}},
	language = {English},
	number = {2},
	urldate = {2020-03-24},
	journal = {European Urology},
	author = {Ploussard, Guillaume and Epstein, Jonathan I. and Montironi, Rodolfo and Carroll, Peter R. and Wirth, Manfred and Grimm, Marc-Oliver and Bjartell, Anders S. and Montorsi, Francesco and Freedland, Stephen J. and Erbersdobler, Andreas and Kwast, Theodorus H. van der},
	month = aug,
	year = {2011},
	pmid = {21601982},
	note = {Publisher: Elsevier},
	pages = {291--303},
	file = {Ploussard et al. - 2011 - The Contemporary Concept of Significant Versus Ins.pdf:/home/duran/Zotero/storage/GKNNUT6M/Ploussard et al. - 2011 - The Contemporary Concept of Significant Versus Ins.pdf:application/pdf;Snapshot:/home/duran/Zotero/storage/8GEZ6J45/pdf.html:text/html}
}

@article{epstein_contemporary_2016,
	title = {A {Contemporary} {Prostate} {Cancer} {Grading} {System}: {A} {Validated} {Alternative} to the {Gleason} {Score}},
	volume = {69},
	issn = {0302-2838},
	shorttitle = {A {Contemporary} {Prostate} {Cancer} {Grading} {System}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5002992/},
	doi = {10.1016/j.eururo.2015.06.046},
	abstract = {Background
Despite revisions in 2005 and 2014, the Gleason prostate cancer (PCa) grading system still has major deficiencies. Combining of Gleason scores into a three-tiered grouping (6, 7, 8–10) is used most frequently for prognostic and therapeutic purposes. The lowest score, assigned 6, may be misunderstood as a cancer in the middle of the grading scale, and 3 + 4 = 7 and 4 + 3 = 7 are often considered the same prognostic group.

Objective
To verify that a new grading system accurately produces a smaller number of grades with the most significant prognostic differences, using multi-institutional and multimodal therapy data.

Design, setting, and participants
Between 2005 and 2014, 20 845 consecutive men were treated by radical prostatectomy at five academic institutions; 5501 men were treated with radiotherapy at two academic institutions.

Outcome measurements and statistical analysis
Outcome was based on biochemical recurrence (BCR). The log-rank test assessed univariable differences in BCR by Gleason score. Separate univariable and multivariable Cox proportional hazards used four possible categorizations of Gleason scores.

Results and limitations
In the surgery cohort, we found large differences in recurrence rates between both Gleason 3 + 4 versus 4 + 3 and Gleason 8 versus 9. The hazard ratios relative to Gleason score 6 were 1.9, 5.1, 8.0, and 11.7 for Gleason scores 3 + 4, 4 + 3, 8, and 9–10, respectively. These differences were attenuated in the radiotherapy cohort as a whole due to increased adjuvant or neoadjuvant hormones for patients with high-grade disease but were clearly seen in patients undergoing radiotherapy only. A five–grade group system had the highest prognostic discrimination for all cohorts on both univariable and multivariable analysis. The major limitation was the unavoidable use of prostate-specific antigen BCR as an end point as opposed to cancer-related death.

Conclusions
The new PCa grading system has these benefits: more accurate grade stratification than current systems, simplified grading system of five grades, and lowest grade is 1, as opposed to 6, with the potential to reduce overtreatment of PCa.

Patient summary
We looked at outcomes for prostate cancer (PCa) treated with radical prostatectomy or radiation therapy and validated a new grading system with more accurate grade stratification than current systems, including a simplified grading system of five grades and a lowest grade is 1, as opposed to 6, with the potential to reduce overtreatment of PCa.},
	number = {3},
	urldate = {2019-08-26},
	journal = {European urology},
	author = {Epstein, Jonathan I. and Zelefsky, Michael J. and Sjoberg, Daniel D. and Nelson, Joel B. and Egevad, Lars and Magi-Galluzzi, Cristina and Vickers, Andrew J. and Parwani, Anil V. and Reuter, Victor E. and Fine, Samson W. and Eastham, James A. and Wiklund, Peter and Han, Misop and Reddy, Chandana A. and Ciezki, Jay P. and Nyberg, Tommy and Klein, Eric A.},
	month = mar,
	year = {2016},
	pmid = {26166626},
	pmcid = {PMC5002992},
	pages = {428--435},
	file = {PubMed Central Full Text PDF:/home/duran/Zotero/storage/6NQ5YASJ/Epstein et al. - 2016 - A Contemporary Prostate Cancer Grading System A V.pdf:application/pdf}
}

@article{cao_joint_2019,
	title = {Joint {Prostate} {Cancer} {Detection} and {Gleason} {Score} {Prediction} in mp-{MRI} via {FocalNet}},
	url = {https://escholarship.org/uc/item/9zt9g3wt},
	doi = {10.1109/TMI.2019.2901928},
	abstract = {Author(s): Cao, Ruiming; Mohammadian Bajgiran, Amirhossein; Afshari Mirak, Sohrab; Shakeri, Sepideh; Zhong, Xinran; Enzmann, Dieter; Raman, Steven; Sung, Kyunghyun},
	language = {en},
	urldate = {2019-08-23},
	journal = {IEEE Transactions on Medical Imaging},
	shortjournal = {IEEE TMI},
	author = {Cao, Ruiming and Mohammadian Bajgiran, Amirhossein and Afshari Mirak, Sohrab and Shakeri, Sepideh and Zhong, Xinran and Enzmann, Dieter and Raman, Steven and Sung, Kyunghyun},
	month = apr,
	year = {2019},
	file = {Full Text PDF:/home/duran/Zotero/storage/29H4W2K5/Cao et al. - 2019 - Joint Prostate Cancer Detection and Gleason Score .pdf:application/pdf;Snapshot:/home/duran/Zotero/storage/ILUEDJCA/9zt9g3wt.html:text/html}
}

@article{abraham_computer-aided_2018,
	title = {Computer-aided classification of prostate cancer grade groups from {MRI} images using texture features and stacked sparse autoencoder},
	volume = {69},
	issn = {0895-6111},
	url = {http://www.sciencedirect.com/science/article/pii/S0895611118304786},
	doi = {10.1016/j.compmedimag.2018.08.006},
	abstract = {A novel method to determine the Grade Group (GG) in prostate cancer (PCa) using multi-parametric magnetic resonance imaging (mpMRI) biomarkers is investigated in this paper. In this method, high-level features are extracted from hand-crafted texture features using a deep network of stacked sparse autoencoders (SSAE) and classified them using a softmax classifier (SMC). Transaxial T2 Weighted (T2W), Apparent Diffusion Coefficient (ADC) and high B-Value Diffusion-Weighted (BVAL) images obtained from PROSTATEx-2 2017 challenge dataset are used in this technique. The method was evaluated on the challenge dataset composed of a training set of 112 lesions and a test set of 70 lesions. It achieved a quadratic-weighted Kappa score of 0.2772 on evaluation using test dataset of the challenge. It also reached a Positive Predictive Value (PPV) of 80\% in predicting PCa with GG {\textgreater} 1. The method achieved first place in the challenge, winning over 43 methods submitted by 21 groups. A 3-fold cross-validation using training data of the challenge was further performed and the method achieved a quadratic-weighted kappa score of 0.2326 and Positive Predictive Value (PPV) of 80.26\% in predicting PCa with GG {\textgreater} 1. Even though the training dataset is a highly imbalanced one, the method was able to achieve a fair kappa score. Being one of the pioneer methods which attempted to classify prostate cancer into 5 grade groups from MRI images, it could serve as a base method for further investigations and improvements.},
	urldate = {2019-07-29},
	journal = {Computerized Medical Imaging and Graphics},
	author = {Abraham, Bejoy and Nair, Madhu S.},
	month = nov,
	year = {2018},
	keywords = {Deep learning, Prostate cancer, Gleason grade, Multiparametric MRI, Stacked sparse autoencoder, Texture features},
	pages = {60--68},
	file = {Abraham and Nair - 2018 - Computer-aided classification of prostate cancer g.pdf:/home/duran/Zotero/storage/EDYBTN37/Abraham and Nair - 2018 - Computer-aided classification of prostate cancer g.pdf:application/pdf;ScienceDirect Snapshot:/home/duran/Zotero/storage/2GDT98IN/S0895611118304786.html:text/html}
}

@inproceedings{hosseinzadeh:MIDLAbstract2019a,
title={Effect of Adding Probabilistic Zonal Prior in Deep Learning-based Prostate Cancer Detection},
author={Matin Hosseinzadeh and Patrick Brand and Henkjan Huisman},
booktitle={International Conference on Medical Imaging with Deep Learning -- Extended Abstract Track},
address={London, United Kingdom},
year={2019},
month={08--10 Jul},
url={https://openreview.net/forum?id=SkxAwFtEqV},
abstract={We propose and evaluate a novel method for automatically detecting clinically significant prostate cancer (csPCa) in bi-parametric magnetic resonance imaging (bpMRI). Prostate zones play an important role in the assessment of prostate cancer on MRI. We hypothesize that the inclusion of zonal information can improve the performance of a deep learning based csPCa lesion detection model. However, segmentation of prostate zones is challenging and therefore deterministic models are inaccurate. Hence, we investigated probabilistic zonal segmentation. Our baseline detection model is a 2DUNet trained to produce a csPCa heatmap followed by a 3D detector. We experimented with the integration of zonal prior information by fusing the output of an anisotropic 3DUNet trained to produce either a deterministic or probabilistic map for each prostate zone. We also investigate the effect of early or late fusion on csPCa detection. All methods were trained and tested on 848 bpMRI. The results show that fusing zonal prior knowledge improves the baseline detection model with a preference for probabilistic over deterministic zonal segmentation.},
}

@inproceedings{vente:MIDLAbstract2019a,
title={Simultaneous Detection and Grading of Prostate Cancer in Multi-Parametric {\{}MRI{\}}},
author={Coen de Vente and Pieter Vos and Josien Pluim and Mitko Veta},
booktitle={International Conference on Medical Imaging with Deep Learning -- Extended Abstract Track},
address={London, United Kingdom},
year={2019},
month={08--10 Jul},
url={https://openreview.net/forum?id=r1lqYQvAY4},
abstract={The goal of this work is to detect prostate cancer (PCa) from multi-parametric MRI (mpMRI) and to simultaneously predict the Gleason Grade Group (GGG) of the detected tumors. We used the ProstateX-2 dataset, for training, validation and testing. The challenge training set contains 99 patients and 112 lesions. The challenge test set contains 63 patients and 70 lesions. T2-weighted and apparent diffusion coefficient images were used as input for a U-Net model. For each tumor a GGG was assigned based on biopsy and pathologic analysis. Segmentation maps of the tumors that were multiplied with a scaled value of the GGG, were used as target of the network, turning the problem into a binary soft-label ordinal regression problem. Using 5-fold cross validation, a voxel-wise quadratic-weighted kappa score of 0.391 {\ensuremath{\pm}} 0.062 and a DSC (GGG {\ensuremath{\geq}} 2) of 0.321 {\ensuremath{\pm}} 0.039 were achieved. In order to evaluate using the challenge test set, the voxel-wise predictions were converted into a single GGG prediction per lesion. Our method ranks higher than 30 out of 43 participants of the challenge with a lesion-wise quadratic weighted kappa score of 0.082 {\ensuremath{\pm}} 0.272 when evaluating on the ProstateX-2 test set. Despite solving a more difficult, but more clinically relevant, problem than the original ProstateX-2 challenge, a relatively high score was achieved using an approach to both grade and detect prostate cancer directly from mpMRI.},
}

@inproceedings{tsehay_biopsy-guided_2017,
	title = {Biopsy-guided learning with deep convolutional neural networks for {Prostate} {Cancer} detection on multiparametric {MRI}},
	doi = {10.1109/ISBI.2017.7950602},
	abstract = {Prostate Cancer (PCa) is highly prevalent and is the second most common cause of cancer-related deaths in men. Multiparametric MRI (mpMRI) is robust in detecting PCa. We developed a weakly supervised computer-aided detection (CAD) system that uses biopsy points to learn to identify PCa on mpMRI. Our CAD system, which is based on a deep convolutional neural network architecture, yielded an area under the curve (AUC) of 0.903±0.009 on a receiver operation characteristic (ROC) curve computed on 10 different models in a 10 fold cross-validation. 9 of the 10 ROCs were statistically significantly different from a competing support vector machine based CAD, which yielded a 0.86 AUC when tested on the same dataset (α = 0.05). Furthermore, our CAD system proved to be more robust in detecting high-grade transition zone lesions.},
	booktitle = {2017 {IEEE} 14th {International} {Symposium} on {Biomedical} {Imaging} ({ISBI} 2017)},
	author = {Tsehay, Y. and Lay, N. and Wang, X. and Kwak, J. T. and Turkbey, B. and Choyke, P. and Pinto, P. and Wood, B. and Summers, R. M.},
	month = apr,
	year = {2017},
	keywords = {Prostate, biomedical MRI, cancer, Lesions, medical image processing, neural nets, Principal component analysis, learning (artificial intelligence), Solid modeling, Training, deep convolutional neural network, Computer-Aided Detection, Biopsy, computer-aided detection, prostate cancer detection, Biopsy Database, biopsy-guided learning, Databases, Holistically-nested Edge Detection, multiparametric MRI, Prostate-CAD, Radiology, receiver operation characteristic curve, sensitivity analysis, support vector machine, support vector machines},
	pages = {642--645},
	file = {IEEE Xplore Abstract Record:/home/duran/Zotero/storage/8IXL7DW5/7950602.html:text/html;Tsehay et al. - 2017 - Biopsy-guided learning with deep convolutional neu.pdf:/home/duran/Zotero/storage/VQW5U9NL/Tsehay et al. - 2017 - Biopsy-guided learning with deep convolutional neu.pdf:application/pdf}
}

@article{yang_co-trained_2017,
	title = {Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric {MRI}},
	volume = {42},
	issn = {1361-8415},
	url = {http://www.sciencedirect.com/science/article/pii/S1361841517301299},
	doi = {10.1016/j.media.2017.08.006},
	abstract = {Multi-parameter magnetic resonance imaging (mp-MRI) is increasingly popular for prostate cancer (PCa) detection and diagnosis. However, interpreting mp-MRI data which typically contains multiple unregistered 3D sequences, e.g. apparent diffusion coefficient (ADC) and T2-weighted (T2w) images, is time-consuming and demands special expertise, limiting its usage for large-scale PCa screening. Therefore, solutions to computer-aided detection of PCa in mp-MRI images are highly desirable. Most recent advances in automated methods for PCa detection employ a handcrafted feature based two-stage classification flow, i.e. voxel-level classification followed by a region-level classification. This work presents an automated PCa detection system which can concurrently identify the presence of PCa in an image and localize lesions based on deep convolutional neural network (CNN) features and a single-stage SVM classifier. Specifically, the developed co-trained CNNs consist of two parallel convolutional networks for ADC and T2w images respectively. Each network is trained using images of a single modality in a weakly-supervised manner by providing a set of prostate images with image-level labels indicating only the presence of PCa without priors of lesions’ locations. Discriminative visual patterns of lesions can be learned effectively from clutters of prostate and surrounding tissues. A cancer response map with each pixel indicating the likelihood to be cancerous is explicitly generated at the last convolutional layer of the network for each modality. A new back-propagated error E is defined to enforce both optimized classification results and consistent cancer response maps for different modalities, which help capture highly representative PCa-relevant features during the CNN feature learning process. The CNN features of each modality are concatenated and fed into a SVM classifier. For images which are classified to contain cancers, non-maximum suppression and adaptive thresholding are applied to the corresponding cancer response maps for PCa foci localization. Evaluation based on 160 patient data with 12-core systematic TRUS-guided prostate biopsy as the reference standard demonstrates that our system achieves a sensitivity of 0.46, 0.92 and 0.97 at 0.1, 1 and 10 false positives per normal/benign patient which is significantly superior to two state-of-the-art CNN-based methods (Oquab et al., 2015; Zhou et al., 2015) and 6-core systematic prostate biopsies.},
	urldate = {2019-07-29},
	journal = {Medical Image Analysis},
	shortjournal = {MEDIA},
	author = {Yang, Xin and Liu, Chaoyue and Wang, Zhiwei and Yang, Jun and Min, Hung Le and Wang, Liang and Cheng, Kwang-Ting (Tim)},
	month = dec,
	year = {2017},
	keywords = {Cancer response map, Co-trained CNN, Convolutional neural network, Prostate biopsy, Prostate cancer detection},
	pages = {212--227},
	file = {ScienceDirect Snapshot:/home/duran/Zotero/storage/LHB9EC9G/S1361841517301299.html:text/html;Yang et al. - 2017 - Co-trained convolutional neural networks for autom.pdf:/home/duran/Zotero/storage/T257IYM9/Yang et al. - 2017 - Co-trained convolutional neural networks for autom.pdf:application/pdf}
}

@article{wang_automated_2018,
	title = {Automated {Detection} of {Clinically} {Significant} {Prostate} {Cancer} in mp-{MRI} {Images} {Based} on an {End}-to-{End} {Deep} {Neural} {Network}},
	volume = {37},
	issn = {0278-0062},
	doi = {10.1109/TMI.2017.2789181},
	abstract = {Automated methods for detecting clinically significant (CS) prostate cancer (PCa) in multi-parameter magnetic resonance images (mp-MRI) are of high demand. Existing methods typically employ several separate steps, each of which is optimized individually without considering the error tolerance of other steps. As a result, they could either involve unnecessary computational cost or suffer from errors accumulated over steps. In this paper, we present an automated CS PCa detection system, where all steps are optimized jointly in an end-to-end trainable deep neural network. The proposed neural network consists of concatenated subnets: 1) a novel tissue deformation network (TDN) for automated prostate detection and multimodal registration and 2) a dual-path convolutional neural network (CNN) for CS PCa detection. Three types of loss functions, i.e., classification loss, inconsistency loss, and overlap loss, are employed for optimizing all parameters of the proposed TDN and CNN. In the training phase, the two nets mutually affect each other and effectively guide registration and extraction of representative CS PCa-relevant features to achieve results with sufficient accuracy. The entire network is trained in a weakly supervised manner by providing only image-level annotations (i.e., presence/absence of PCa) without exact priors of lesions' locations. Compared with most existing systems which require supervised labels, e.g., manual delineation of PCa lesions, it is much more convenient for clinical usage. Comprehensive evaluation based on fivefold cross validation using 360 patient data demonstrates that our system achieves a high accuracy for CS PCa detection, i.e., a sensitivity of 0.6374 and 0.8978 at 0.1 and 1 false positives per normal/benign patient.},
	number = {5},
	journal = {IEEE Transactions on Medical Imaging},
	shortjournal = {IEEE TMI},
	author = {Wang, Z. and Liu, C. and Cheng, D. and Wang, L. and Yang, X. and Cheng, K.},
	month = may,
	year = {2018},
	keywords = {Prostate, Feature extraction, Algorithms, automated CS PCa detection system, automated prostate detection, biological tissues, biomedical MRI, cancer, Cancer, CS PCa detection, dual-path convolutional neural network, end-to-end deep neural network, feature extraction, Humans, Image Interpretation, Computer-Assisted, image registration, image-level annotations, joint optimization, Lesions, Magnetic Resonance Imaging, Male, Manuals, medical image processing, mp-MRI images, multimodal registration, multiparameter magnetic resonance images, neural nets, neural network, Neural networks, Neural Networks (Computer), PCa lesions, Principal component analysis, prostate cancer, Prostatic Neoplasms, representative CS PCa-relevant features, ROC Curve, tissue deformation network},
	pages = {1127--1139},
	file = {IEEE Xplore Abstract Record:/home/duran/Zotero/storage/JZR8HF6G/8245842.html:text/html;Wang et al. - 2018 - Automated Detection of Clinically Significant Pros.pdf:/home/duran/Zotero/storage/IN2SUIWG/Wang et al. - 2018 - Automated Detection of Clinically Significant Pros.pdf:application/pdf}
}

@article{schlemper_attention_2019,
	title = {Attention gated networks: {Learning} to leverage salient regions in medical images},
	volume = {53},
	issn = {1361-8415},
	shorttitle = {Attention gated networks},
	url = {http://www.sciencedirect.com/science/article/pii/S1361841518306133},
	doi = {10.1016/j.media.2019.01.012},
	abstract = {We propose a novel attention gate (AG) model for medical image analysis that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly learn to suppress irrelevant regions in an input image while highlighting salient features useful for a specific task. This enables us to eliminate the necessity of using explicit external tissue/organ localisation modules when using convolutional neural networks (CNNs). AGs can be easily integrated into standard CNN models such as VGG or U-Net architectures with minimal computational overhead while increasing the model sensitivity and prediction accuracy. The proposed AG models are evaluated on a variety of tasks, including medical image classification and segmentation. For classification, we demonstrate the use case of AGs in scan plane detection for fetal ultrasound screening. We show that the proposed attention mechanism can provide efficient object localisation while improving the overall prediction performance by reducing false positives. For segmentation, the proposed architecture is evaluated on two large 3D CT abdominal datasets with manual annotations for multiple organs. Experimental results show that AG models consistently improve the prediction performance of the base architectures across different datasets and training sizes while preserving computational efficiency. Moreover, AGs guide the model activations to be focused around salient regions, which provides better insights into how model predictions are made. The source code for the proposed AG models is publicly available.},
	language = {en},
	urldate = {2019-11-12},
	journal = {Medical Image Analysis},
	author = {Schlemper, Jo and Oktay, Ozan and Schaap, Michiel and Heinrich, Mattias and Kainz, Bernhard and Glocker, Ben and Rueckert, Daniel},
	month = apr,
	year = {2019},
	keywords = {Segmentation, Attention gates, Fully convolutional networks, Image classification, Localisation, Soft attention},
	pages = {197--207},
	file = {ScienceDirect Full Text PDF:/home/duran/Zotero/storage/J4CB4DSP/Schlemper et al. - 2019 - Attention gated networks Learning to leverage sal.pdf:application/pdf;ScienceDirect Snapshot:/home/duran/Zotero/storage/QBS2F4Z6/S1361841518306133.html:text/html}
}

@article{ronneberger_u-net:_2015,
	title = {U-{Net}: {Convolutional} {Networks} for {Biomedical} {Image} {Segmentation}},
	shorttitle = {U-{Net}},
	url = {http://arxiv.org/abs/1505.04597},
	abstract = {There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .},
	urldate = {2019-05-14},
	journal = {arXiv:1505.04597 [cs]},
	author = {Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
	month = may,
	year = {2015},
	note = {arXiv: 1505.04597},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	file = {arXiv\:1505.04597 PDF:/home/duran/Zotero/storage/2IBSWZDN/Ronneberger et al. - 2015 - U-Net Convolutional Networks for Biomedical Image.pdf:application/pdf;arXiv.org Snapshot:/home/duran/Zotero/storage/UCIK3LGG/1505.html:text/html}
}

@inproceedings{Maas2013RectifierNI,
  title={Rectifier Nonlinearities Improve Neural Network Acoustic Models},
  author = {Andrew L. Maas and Awni Y. Hannun and Andrew Y. Ng},
  booktitle = {ICML Workshop on Deep Learning for Audio, Speech and Language Processing},
  year={2013}
}

@article{Ioffe2015BatchNA,
  title={Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift},
  author={Sergey Ioffe and Christian Szegedy},
  journal={ArXiv},
  year={2015},
  volume={abs/1502.03167}
}

@ARTICLE{2014arXiv1412.6980K,
       author = {{Kingma}, Diederik P. and {Ba}, Jimmy},
        title = "{Adam: A Method for Stochastic Optimization}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Machine Learning},
         year = "2014",
        month = "Dec",
          eid = {arXiv:1412.6980},
        pages = {arXiv:1412.6980},
archivePrefix = {arXiv},
       eprint = {1412.6980},
 primaryClass = {cs.LG},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2014arXiv1412.6980K},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@article{azizi_detection_2017,
	title = {Detection and grading of prostate cancer using temporal enhanced ultrasound: combining deep neural networks and tissue mimicking simulations},
	volume = {12},
	issn = {1861-6410, 1861-6429},
	shorttitle = {Detection and grading of prostate cancer using temporal enhanced ultrasound},
	url = {http://link.springer.com/10.1007/s11548-017-1627-0},
	doi = {10.1007/s11548-017-1627-0},
	language = {en},
	number = {8},
	urldate = {2020-04-16},
	journal = {International Journal of Computer Assisted Radiology and Surgery},
	author = {Azizi, Shekoofeh and Bayat, Sharareh and Yan, Pingkun and Tahmasebi, Amir and Nir, Guy and Kwak, Jin Tae and Xu, Sheng and Wilson, Storey and Iczkowski, Kenneth A. and Lucia, M. Scott and Goldenberg, Larry and Salcudean, Septimiu E. and Pinto, Peter A. and Wood, Bradford and Abolmaesumi, Purang and Mousavi, Parvin},
	month = aug,
	year = {2017},
	pages = {1293--1305}
}

@misc{chollet2015keras,
  title={Keras},
  author={Chollet, Fran\c{c}ois and others},
  year={2015},
  howpublished={\url{https://keras.io}},
}

@misc{tensorflow2015-whitepaper,
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
    Yangqing Jia 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},
}