@book{Lamport:Book:1989,
 author = {Lamport, Leslie},
 title = {Latex: A Document Preparation System},
 year = {1986},
 isbn = {0-201-15790-X},
 publisher = {Addison-Wesley Longman Publishing Co., Inc.},
 address = {Boston, MA, USA},
}

@misc{Hinton:arXiv:2015:Distilling,
 title={Distilling the knowledge in a neural network},
 author={Hinton, Geoffrey and Vinyals, Oriol and Dean, Jeff},
 OPTeprinttype={arXiv},
 OPTeprint={1503.02531},
 howpublished={Eprint \href{http://arxiv.org/abs/1503.02531}{arXiv:1503.02531}},
 OPTurl={http://arxiv.org/abs/1503.02531},
 year=2015
}

@article{wei_diagnostic_2021,
	title = {Diagnostic {Accuracy} and {Interobserver} {Agreement} of {PI}-{RADS} {Version} 2 and {Version} 2.1 for the {Detection} of {Transition} {Zone} {Prostate} {Cancers}},
	volume = {216},
	issn = {0361-803X},
	url = {https://www.ajronline.org/doi/10.2214/AJR.20.23883},
	doi = {10.2214/AJR.20.23883},
	abstract = {BACKGROUND. PI-RADS version 2.1 (v2.1) introduced a number of key changes to the assessment of transition zone (TZ) lesions.OBJECTIVE. The purpose of this study was to evaluate interobserver agreement and diagnostic accuracy for detecting TZ prostate cancer (PCa) and clinically significant PCa (csPCa) by use of PI-RADS v2 and PI-RADS v2.1 among radiologists with different levels of experience.METHODS. This retrospective study included 355 biopsy-naïve patients who from January 2017 to March 2020 underwent prostate MRI that showed a TZ lesion and underwent subsequent biopsy. PCa was diagnosed in 93 patients (International Society of Urological Pathology [ISUP] grade group 1, n = 34; ISUP grade group ≥ 2, n = 59) and non-cancerous lesions in 262 patients. Five radiologists with varying experience in prostate MRI scored lesions using PI-RADS v2 and PI-RADS v2.1 in sessions separated by at least 4 weeks. Interobserver agreement was evaluated with kappa and Kendall W statistics. ROC curve analysis was used to evaluate performance in detection of TZ PCa and csPCa.RESULTS. Interobserver agreement among all readers was higher for PI-RADS v2.1 than for PI-RADS v2 (mean weighted κ = 0.700 vs 0.622; Kendall W = 0.805 vs 0.728; p = .03). The pooled AUC values for detecting TZ PCa and csPCa were higher among all readers using PI-RADS v2.1 (0.866 vs 0.827 for TZ PCa; 0.929 vs 0.899 for TZ csPCa; p {\textless} .001). For detecting TZ PCa, the pooled sensitivity, specificity, and accuracy were 86.9\%, 79.4\%, and 75.4\% among all readers for PI-RADS v2.1 compared with 79.4\%, 71.8\%, and 73.8\% for PI-RADS v2. For detecting TZ csPCa, the pooled sensitivity, specificity, and accuracy were 84.8\%, 90.9\%, and 89.9\% among all readers for PI-RADS v2.1 compared with 81.4\%, 89.9\%, and 88.5\% for PI-RADS v2. Reader 1, who had the least experience, had the lowest sensitivity, specificity, and accuracy (78.0\%, 89.2\%, and 87.3\%). Reader 5, who had the most experience, had the highest sensitivity, specificity, and accuracy (88.1\%, 92.9\%, and 92.1\%) in detecting csPCa.CONCLUSION. PI-RADS v2.1 had better interobserver agreement and diagnostic accuracy than PI-RADS v2 for evaluating TZ lesions. Reader experience continues to affect the performance of prostate MRI interpretation with PI-RADS v2.1.CLINICAL IMPACT. PI-RADS v2.1 is more accurate and reproducible than PI-RADS v2 for the diagnosis of TZ PCa.},
	number = {5},
	urldate = {2025-01-03},
	journal = {American Journal of Roentgenology},
	author = {Wei, Chao-gang and Zhang, Yue-yue and Pan, Peng and Chen, Tong and Yu, Hong-chang and Dai, Guang-cheng and Tu, Jian and Yang, Shuo and Zhao, Wen-lu and Shen, Jun-kang},
	month = may,
	year = {2021},
	note = {Publisher: American Roentgen Ray Society},
	pages = {1247--1256},
}

@article{park_performance_2021,
	title = {Performance of {Prostate} {Imaging} {Reporting} and {Data} {System} {Version} 2.1 for {Diagnosis} of {Prostate} {Cancer}: {A} {Systematic} {Review} and {Meta}-{Analysis}},
	volume = {54},
	copyright = {© 2021 International Society for Magnetic Resonance in Medicine},
	issn = {1522-2586},
	shorttitle = {Performance of {Prostate} {Imaging} {Reporting} and {Data} {System} {Version} 2.1 for {Diagnosis} of {Prostate} {Cancer}},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jmri.27546},
	doi = {10.1002/jmri.27546},
	abstract = {Background The Prostate Imaging Reporting and Data System (PI-RADS) was introduced in 2012 and updated to version 2.1 (v2.1) in early 2019 to improve diagnostic performance and interreader reliability. Purpose To evaluate the diagnostic performance of PI-RADS v2.1 in comparison with v2. Methods A systematic review and meta-analysis of the literature was performed using MEDLINE, EMBASE, and Cochrane databases to identify studies evaluating the diagnostic performance of PI-RADS v2.1 for diagnosing clinically significant prostate cancer (csPCa). Study Type Systematic review and meta-analysis. Subject One thousand two hundred forty-eight patients with 1406 lesions from 10 eligible articles. Field Strength/sequence Conventional MR sequences at 1.5 T and 3 T. Assessment Two reviewers independently identified and reviewed the original articles reporting diagnostic performance of PI-RADS v2.1. Statistical Tests Meta-analytic summary sensitivity and specificity were calculated using a bivariate random effects model. Meta-analytic sensitivity and specificity between PI-RADS v2 and v2.1 were compared. Results The pooled sensitivity and specificity of PI-RADS v2.1 were 87\% (95\% confidence intervals, 82–91\%) and 74\% (63–82\%), respectively. In five studies available for a head-to-head comparison between PI-RADS v2.1 and v2, there were no significant differences in either sensitivity (90\% [86–94\%] vs. 88\% [83–93\%], respectively) or specificity (76\% [59–93\%] vs. 61\% [39–83\%], respectively; P = 0.37). The sensitivity and specificity were 81\% (73–87\%) and 82\% (68–91\%), respectively, for a PI-RADS score cutoff of ≥4, and 94\% (88–97\%) and 56\% (35–97\%) for ≥3. Regarding the zonal location, the sensitivity and specificity for the transitional zone only were 90\% (84–96\%) and 76\% (62–90\%) respectively, whereas for the whole gland they were 85\% (79–91\%) and 71\% (57–85\%). Data Conclusion PI-RADS v2.1 demonstrated good overall performance for the diagnosis of csPCa. PI-RADS v2.1 tended to show higher specificity than v2, but the difference lacked statistical significance. Level of Evidence 3 Technical Efficacy Stage 3},
	language = {en},
	number = {1},
	urldate = {2025-01-03},
	journal = {Journal of Magnetic Resonance Imaging},
	author = {Park, Kye Jin and Choi, Sang Hyun and Kim, Mi-hyun and Kim, Jeong Kon and Jeong, In Gab},
	year = {2021},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/jmri.27546},
	keywords = {PI-RADS, diagnostic performance, prostate, version 2.1},
	pages = {103--112},
}

@article{borghesi_complications_2017,
	title = {Complications {After} {Systematic}, {Random}, and {Image}-guided {Prostate} {Biopsy}},
	volume = {71},
	issn = {1873-7560},
	doi = {10.1016/j.eururo.2016.08.004},
	abstract = {CONTEXT: Prostate biopsy (PB) represents the gold standard method to confirm the presence of cancer. In addition to traditional random or systematic approaches, a magnetic resonance imaging (MRI)-guided technique has been introduced recently.
OBJECTIVE: To perform a systematic review of complications after transrectal ultrasound (TRUS)-guided, transperineal, and MRI-guided PB.
EVIDENCE ACQUISITION: We performed a systematic literature search of Web of Science, Embase, and Scopus databases up to October 2015, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Complications and mortality following random, systematic, and image-guided PBs were reviewed. Eighty-five references were included.
EVIDENCE SYNTHESIS: The most frequent complication after PB was minor and self-limiting bleeding (hematuria and hematospermia), regardless of the biopsy approach. Occurrence of rectal bleeding was comparable for traditional TRUS-guided and image-guided PBs. Almost 25\% of patients experienced lower urinary tract symptoms, but only a few had urinary retention, with higher rates after a transperineal approach. Temporary erectile dysfunction was not negligible, with a return to baseline after 1-6 mo. The incidence of infective complications is increasing, with higher rates among men with medical comorbidities and older age. Transperineal and in-bore MRI-targeted biopsy may reduce the risk of severe infectious complications. Mortality after PB is uncommon, regardless of biopsy technique.
CONCLUSIONS: Complications after PB are frequent but often self-limiting. The incidence of hospitalization due to severe infections is continuously increasing. The patient's general health status, risk factors, and likelihood of antimicrobial resistance should be carefully appraised before scheduling a PB.
PATIENT SUMMARY: We reviewed the variety and incidence of complications after prostate biopsy. Even if frequent, complications seldom represent a problem for the patient. The most troublesome complications are infections. To minimize this risk, the patient's medical condition should be carefully evaluated before biopsy.},
	language = {eng},
	number = {3},
	journal = {European Urology},
	author = {Borghesi, Marco and Ahmed, Hashim and Nam, Robert and Schaeffer, Edward and Schiavina, Riccardo and Taneja, Samir and Weidner, Wolfgang and Loeb, Stacy},
	month = mar,
	year = {2017},
	pmid = {27543165},
	keywords = {Biopsy, Complications, Endosonography, Erectile Dysfunction, Hematuria, Hemospermia, Hospitalization, Humans, Image-Guided Biopsy, Infection, Lower Urinary Tract Symptoms, MRI-guided biopsy, Magnetic Resonance Imaging, Male, Mortality, Postoperative Complications, Postoperative Hemorrhage, Prostate, Prostate biopsy, Prostatic Neoplasms, Recovery of Function, Rectal Diseases, Surgical Wound Infection, Urinary Retention},
	pages = {353--365},
}

@article{bray_global_2024,
	title = {Global cancer statistics 2022: {GLOBOCAN} estimates of incidence and mortality worldwide for 36 cancers in 185 countries},
	volume = {74},
	copyright = {© 2024 The Authors. CA: A Cancer Journal for Clinicians published by Wiley Periodicals LLC on behalf of American Cancer Society.},
	issn = {1542-4863},
	shorttitle = {Global cancer statistics 2022},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.3322/caac.21834},
	doi = {10.3322/caac.21834},
	abstract = {This article presents global cancer statistics by world region for the year 2022 based on updated estimates from the International Agency for Research on Cancer (IARC). There were close to 20 million new cases of cancer in the year 2022 (including nonmelanoma skin cancers [NMSCs]) alongside 9.7 million deaths from cancer (including NMSC). The estimates suggest that approximately one in five men or women develop cancer in a lifetime, whereas around one in nine men and one in 12 women die from it. Lung cancer was the most frequently diagnosed cancer in 2022, responsible for almost 2.5 million new cases, or one in eight cancers worldwide (12.4\% of all cancers globally), followed by cancers of the female breast (11.6\%), colorectum (9.6\%), prostate (7.3\%), and stomach (4.9\%). Lung cancer was also the leading cause of cancer death, with an estimated 1.8 million deaths (18.7\%), followed by colorectal (9.3\%), liver (7.8\%), female breast (6.9\%), and stomach (6.8\%) cancers. Breast cancer and lung cancer were the most frequent cancers in women and men, respectively (both cases and deaths). Incidence rates (including NMSC) varied from four-fold to five-fold across world regions, from over 500 in Australia/New Zealand (507.9 per 100,000) to under 100 in Western Africa (97.1 per 100,000) among men, and from over 400 in Australia/New Zealand (410.5 per 100,000) to close to 100 in South-Central Asia (103.3 per 100,000) among women. The authors examine the geographic variability across 20 world regions for the 10 leading cancer types, discussing recent trends, the underlying determinants, and the prospects for global cancer prevention and control. With demographics-based predictions indicating that the number of new cases of cancer will reach 35 million by 2050, investments in prevention, including the targeting of key risk factors for cancer (including smoking, overweight and obesity, and infection), could avert millions of future cancer diagnoses and save many lives worldwide, bringing huge economic as well as societal dividends to countries over the forthcoming decades.},
	language = {en},
	number = {3},
	urldate = {2025-01-03},
	journal = {CA: A Cancer Journal for Clinicians},
	author = {Bray, Freddie and Laversanne, Mathieu and Sung, Hyuna and Ferlay, Jacques and Siegel, Rebecca L. and Soerjomataram, Isabelle and Jemal, Ahmedin},
	year = {2024},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.3322/caac.21834},
	keywords = {cancer burden, cancer control, epidemiology, incidence, mortality},
	pages = {229--263},
}

@misc{vaswani_attention_2023,
	title = {Attention {Is} {All} {You} {Need}},
	url = {http://arxiv.org/abs/1706.03762},
	doi = {10.48550/arXiv.1706.03762},
	abstract = {The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.},
	urldate = {2024-12-05},
	publisher = {arXiv},
	author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Lukasz and Polosukhin, Illia},
	month = aug,
	year = {2023},
	note = {arXiv:1706.03762 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@misc{tay_long_2020,
	title = {Long {Range} {Arena}: {A} {Benchmark} for {Efficient} {Transformers}},
	shorttitle = {Long {Range} {Arena}},
	url = {http://arxiv.org/abs/2011.04006},
	doi = {10.48550/arXiv.2011.04006},
	abstract = {Transformers do not scale very well to long sequence lengths largely because of quadratic self-attention complexity. In the recent months, a wide spectrum of efficient, fast Transformers have been proposed to tackle this problem, more often than not claiming superior or comparable model quality to vanilla Transformer models. To this date, there is no well-established consensus on how to evaluate this class of models. Moreover, inconsistent benchmarking on a wide spectrum of tasks and datasets makes it difficult to assess relative model quality amongst many models. This paper proposes a systematic and unified benchmark, LRA, specifically focused on evaluating model quality under long-context scenarios. Our benchmark is a suite of tasks consisting of sequences ranging from \$1K\$ to \$16K\$ tokens, encompassing a wide range of data types and modalities such as text, natural, synthetic images, and mathematical expressions requiring similarity, structural, and visual-spatial reasoning. We systematically evaluate ten well-established long-range Transformer models (Reformers, Linformers, Linear Transformers, Sinkhorn Transformers, Performers, Synthesizers, Sparse Transformers, and Longformers) on our newly proposed benchmark suite. LRA paves the way towards better understanding this class of efficient Transformer models, facilitates more research in this direction, and presents new challenging tasks to tackle. Our benchmark code will be released at https://github.com/google-research/long-range-arena.},
	urldate = {2024-12-05},
	publisher = {arXiv},
	author = {Tay, Yi and Dehghani, Mostafa and Abnar, Samira and Shen, Yikang and Bahri, Dara and Pham, Philip and Rao, Jinfeng and Yang, Liu and Ruder, Sebastian and Metzler, Donald},
	month = nov,
	year = {2020},
	note = {arXiv:2011.04006 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Information Retrieval, Computer Science - Machine Learning},
}

@misc{gu_hippo_2020,
	title = {{HiPPO}: {Recurrent} {Memory} with {Optimal} {Polynomial} {Projections}},
	shorttitle = {{HiPPO}},
	url = {http://arxiv.org/abs/2008.07669},
	doi = {10.48550/arXiv.2008.07669},
	abstract = {A central problem in learning from sequential data is representing cumulative history in an incremental fashion as more data is processed. We introduce a general framework (HiPPO) for the online compression of continuous signals and discrete time series by projection onto polynomial bases. Given a measure that specifies the importance of each time step in the past, HiPPO produces an optimal solution to a natural online function approximation problem. As special cases, our framework yields a short derivation of the recent Legendre Memory Unit (LMU) from first principles, and generalizes the ubiquitous gating mechanism of recurrent neural networks such as GRUs. This formal framework yields a new memory update mechanism (HiPPO-LegS) that scales through time to remember all history, avoiding priors on the timescale. HiPPO-LegS enjoys the theoretical benefits of timescale robustness, fast updates, and bounded gradients. By incorporating the memory dynamics into recurrent neural networks, HiPPO RNNs can empirically capture complex temporal dependencies. On the benchmark permuted MNIST dataset, HiPPO-LegS sets a new state-of-the-art accuracy of 98.3\%. Finally, on a novel trajectory classification task testing robustness to out-of-distribution timescales and missing data, HiPPO-LegS outperforms RNN and neural ODE baselines by 25-40\% accuracy.},
	urldate = {2024-12-05},
	publisher = {arXiv},
	author = {Gu, Albert and Dao, Tri and Ermon, Stefano and Rudra, Atri and Re, Christopher},
	month = oct,
	year = {2020},
	note = {arXiv:2008.07669 [cs]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{gu_efficiently_2022,
	title = {Efficiently {Modeling} {Long} {Sequences} with {Structured} {State} {Spaces}},
	url = {http://arxiv.org/abs/2111.00396},
	doi = {10.48550/arXiv.2111.00396},
	abstract = {A central goal of sequence modeling is designing a single principled model that can address sequence data across a range of modalities and tasks, particularly on long-range dependencies. Although conventional models including RNNs, CNNs, and Transformers have specialized variants for capturing long dependencies, they still struggle to scale to very long sequences of \$10000\$ or more steps. A promising recent approach proposed modeling sequences by simulating the fundamental state space model (SSM) {\textbackslash}( x'(t) = Ax(t) + Bu(t), y(t) = Cx(t) + Du(t) {\textbackslash}), and showed that for appropriate choices of the state matrix {\textbackslash}( A {\textbackslash}), this system could handle long-range dependencies mathematically and empirically. However, this method has prohibitive computation and memory requirements, rendering it infeasible as a general sequence modeling solution. We propose the Structured State Space sequence model (S4) based on a new parameterization for the SSM, and show that it can be computed much more efficiently than prior approaches while preserving their theoretical strengths. Our technique involves conditioning {\textbackslash}( A {\textbackslash}) with a low-rank correction, allowing it to be diagonalized stably and reducing the SSM to the well-studied computation of a Cauchy kernel. S4 achieves strong empirical results across a diverse range of established benchmarks, including (i) 91{\textbackslash}\% accuracy on sequential CIFAR-10 with no data augmentation or auxiliary losses, on par with a larger 2-D ResNet, (ii) substantially closing the gap to Transformers on image and language modeling tasks, while performing generation \$60{\textbackslash}times\$ faster (iii) SoTA on every task from the Long Range Arena benchmark, including solving the challenging Path-X task of length 16k that all prior work fails on, while being as efficient as all competitors.},
	urldate = {2024-12-05},
	publisher = {arXiv},
	author = {Gu, Albert and Goel, Karan and Ré, Christopher},
	month = aug,
	year = {2022},
	note = {arXiv:2111.00396 [cs]},
	keywords = {Computer Science - Machine Learning},
}

@article{zurowska_prostate_2023,
	title = {Prostate {Cancer} and {Its} {Mimics}—{A} {Pictorial} {Review}},
	volume = {15},
	number = {14},
	journal = {Cancers},
	author = {Żurowska, Anna and Pęksa, Rafał and Bieńkowski, Michał and Skrobisz, Katarzyna and Sowa, Marek and Matuszewski, Marcin and Biernat, Wojciech and Szurowska, Edyta},
	year = {2023},
	note = {Publisher: MDPI},
	pages = {3682},
}

@article{bach_pixel-wise_2015,
	title = {On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation},
	volume = {10},
	number = {7},
	journal = {PloS one},
	author = {Bach, Sebastian and Binder, Alexander and Montavon, Grégoire and Klauschen, Frederick and Müller, Klaus-Robert and Samek, Wojciech},
	year = {2015},
	note = {Publisher: Public Library of Science San Francisco, CA USA},
	pages = {e0130140},
}

@article{gu_mamba_2023,
	title = {Mamba: {Linear}-time sequence modeling with selective state spaces},
	journal = {arXiv preprint arXiv:2312.00752},
	author = {Gu, Albert and Dao, Tri},
	year = {2023},
}

@article{selvaraju_grad-cam_2020,
	title = {Grad-{CAM}: visual explanations from deep networks via gradient-based localization},
	volume = {128},
	journal = {International journal of computer vision},
	author = {Selvaraju, Ramprasaath R and Cogswell, Michael and Das, Abhishek and Vedantam, Ramakrishna and Parikh, Devi and Batra, Dhruv},
	year = {2020},
	note = {Publisher: Springer},
	pages = {336--359},
}

@inproceedings{ronneberger_u-net_2015,
	title = {U-net: {Convolutional} networks for biomedical image segmentation},
	booktitle = {Medical image computing and computer-assisted intervention–{MICCAI} 2015: 18th international conference, {Munich}, {Germany}, {October} 5-9, 2015, proceedings, part {III} 18},
	publisher = {Springer},
	author = {Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
	year = {2015},
	pages = {234--241},
}

@inproceedings{jaeger_retina_2020,
	title = {Retina {U}-{Net}: {Embarrassingly} simple exploitation of segmentation supervision for medical object detection},
	booktitle = {Machine {Learning} for {Health} {Workshop}},
	publisher = {PMLR},
	author = {Jaeger, Paul F and Kohl, Simon AA and Bickelhaupt, Sebastian and Isensee, Fabian and Kuder, Tristan Anselm and Schlemmer, Heinz-Peter and Maier-Hein, Klaus H},
	year = {2020},
	pages = {171--183},
}

@inproceedings{baumgartner_nndetection_2021,
	title = {{nnDetection}: a self-configuring method for medical object detection},
	booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention}–{MICCAI} 2021: 24th {International} {Conference}, {Strasbourg}, {France}, {September} 27–{October} 1, 2021, {Proceedings}, {Part} {V} 24},
	publisher = {Springer},
	author = {Baumgartner, Michael and Jäger, Paul F and Isensee, Fabian and Maier-Hein, Klaus H},
	year = {2021},
	pages = {530--539},
}

@article{isensee_nnu-net_2021,
	title = {{nnU}-{Net}: a self-configuring method for deep learning-based biomedical image segmentation},
	volume = {18},
	number = {2},
	journal = {Nature methods},
	author = {Isensee, Fabian and Jaeger, Paul F and Kohl, Simon AA and Petersen, Jens and Maier-Hein, Klaus H},
	year = {2021},
	note = {Publisher: Nature Publishing Group},
	pages = {203--211},
}

@misc{falcon_pytorch_2024,
	title = {{PyTorch} {Lightning}},
	url = {https://doi.org/10.5281/zenodo.13254264},
	publisher = {Zenodo},
	author = {Falcon, William and team, The PyTorch Lightning},
	month = aug,
	year = {2024},
	doi = {10.5281/zenodo.13254264},
}

@article{islam_prostate_2024,
	title = {Prostate {Cancer} {Detection} from {MRI} {Using} {Efficient} {Feature} {Extraction} with {Transfer} {Learning}},
	volume = {2024},
	number = {1},
	journal = {Prostate Cancer},
	author = {Islam, Rafiqul and Imran, Al and Rabbi, Md Fazle},
	year = {2024},
	note = {Publisher: Wiley Online Library},
	pages = {1588891},
}

@article{hamm_interactive_2023,
	title = {Interactive explainable deep learning model informs prostate cancer diagnosis at {MRI}},
	volume = {307},
	number = {4},
	journal = {Radiology},
	author = {Hamm, Charlie A and Baumgärtner, Georg L and Biessmann, Felix and Beetz, Nick L and Hartenstein, Alexander and Savic, Lynn J and Froböse, Konrad and Dräger, Franziska and Schallenberg, Simon and Rudolph, Madhuri and {others}},
	year = {2023},
	note = {Publisher: Radiological Society of North America},
	pages = {e222276},
}

@article{ma_u-mamba_2024,
	title = {U-{Mamba}: {Enhancing} {Long}-range {Dependency} for {Biomedical} {Image} {Segmentation}},
	journal = {arXiv preprint arXiv:2401.04722},
	author = {Ma, Jun and Li, Feifei and Wang, Bo},
	year = {2024},
}

@misc{consortium_project_2020,
	title = {Project {MONAI}},
	url = {https://doi.org/10.5281/zenodo.4323059},
	publisher = {Zenodo},
	author = {Consortium, The MONAI},
	month = dec,
	year = {2020},
	doi = {10.5281/zenodo.4323059},
}

@article{gunashekar_explainable_2022,
	title = {Explainable {AI} for {CNN}-based prostate tumor segmentation in multi-parametric {MRI} correlated to whole mount histopathology},
	volume = {17},
	number = {1},
	journal = {Radiation Oncology},
	author = {Gunashekar, Deepa Darshini and Bielak, Lars and Hägele, Leonard and Oerther, Benedict and Benndorf, Matthias and Grosu, Anca-L and Brox, Thomas and Zamboglou, Constantinos and Bock, Michael},
	year = {2022},
	note = {Publisher: Springer},
	pages = {65},
}

@article{karagoz_prostate_2023,
	title = {Prostate lesion estimation using prostate masks from biparametric mri},
	journal = {arXiv preprint arXiv:2301.09673},
	author = {Karagoz, Ahmet and Seker, Mustafa Ege and Yergin, Mert and Kan, Tarkan Atak and Kartal, Mustafa Said and Karaarslan, Ercan and Alis, Deniz and Oksuz, Ilkay},
	year = {2023},
}

@article{debs_deep_nodate,
	title = {Deep learning for detection and diagnosis of prostate cancer from {bpMRI} and {PSA}: {Guerbet}’s contribution to the {PI}-{CAI} 2022 {Grand} {Challenge}},
        year = {2022},
	author = {Debs, Noëlie and Routier, Alexandre and Abi-Nader, Clément and Marcoux, Arnaud and Nicolas, François and Bône, Alexandre and Rohé, Marc-Michel},
}

@misc{debs_deep_2022,
  title        = {Deep learning for detection and diagnosis of prostate cancer from {bpMRI} and {PSA}: {Guerbet}’s contribution to the {PI}-{CAI} 2022 {Grand} {Challenge}},
  author       = {Debs, Noëlie and Routier, Alexandre and Abi-Nader, Clément and Marcoux, Arnaud and Nicolas, François and Bône, Alexandre and Rohé, Marc-Michel},
  year         = {2022},
  note         = {PI-CAI 2022 Grand Challenge Report},
}

@article{saha_artificial_2024,
	title = {Artificial intelligence and radiologists in prostate cancer detection on {MRI} ({PI}-{CAI}): an international, paired, non-inferiority, confirmatory study},
	volume = {25},
	issn = {1470-2045},
	doi = {https://doi.org/10.1016/S1470-2045(24)00220-1},
	number = {7},
	journal = {The Lancet Oncology},
	author = {Saha, Anindo and Bosma, Joeran S. and Twilt, Jasper J. and Ginneken, Bram van and Bjartell, Anders and Padhani, Anwar R. and Bonekamp, David and Villeirs, Geert and Salomon, Georg and Giannarini, Gianluca and Kalpathy-Cramer, Jayashree and Barentsz, Jelle and Maier-Hein, Klaus H. and Rusu, Mirabela and Rouvière, Olivier and Bergh, Roderick van den and Panebianco, Valeria and Kasivisvanathan, Veeru and Obuchowski, Nancy A. and Yakar, Derya and Elschot, Mattijs and Veltman, Jeroen and Fütterer, Jurgen J. and Noordman, Constant R. and Slootweg, Ivan and Roest, Christian and Fransen, Stefan J. and Sunoqrot, Mohammed R. S. and Bathen, Tone F. and Rouw, Dennis and Immerzeel, Jos and Geerdink, Jeroen and Run, Chris van and Groeneveld, Miriam and Meakin, James and Karagöz, Ahmet and Bône, Alexandre and Routier, Alexandre and Marcoux, Arnaud and Abi-Nader, Clément and Li, Cynthia Xinran and Feng, Dagan and Alis, Deniz and Karaarslan, Ercan and Ahn, Euijoon and Nicolas, François and Sonn, Geoffrey A. and Bhattacharya, Indrani and Kim, Jinman and Shi, Jun and Jahanandish, Hassan and An, Hong and Kan, Hongyu and Oksuz, Ilkay and Qiao, Liang and Rohé, Marc-Michel and Yergin, Mert and Khadra, Mohamed and Şeker, Mustafa E. and Kartal, Mustafa S. and Debs, Noëlie and Fan, Richard E. and Saunders, Sara and Soerensen, Simon J. C. and Moroianu, Stefania and Vesal, Sulaiman and Yuan, Yuan and Malakoti-Fard, Afsoun and Mačiūnien, Agnė and Kawashima, Akira and Machadov, Ana M. M. de M. G. de Sousa and Moreira, Ana Sofia L. and Ponsiglione, Andrea and Rappaport, Annelies and Stanzione, Arnaldo and Ciuvasovas, Arturas and Turkbey, Baris and Keyzer, Bart de and Pedersen, Bodil G. and Eijlers, Bram and Chen, Christine and Riccardo, Ciabattoni and Alis, Deniz and Staal, Ewout F. W. Courrech and Jäderling, Fredrik and Langkilde, Fredrik and Aringhieri, Giacomo and Brembilla, Giorgio and Son, Hannah and Vanderlelij, Hans and Raat, Henricus P. J. and Pikūnienė, Ingrida and Macova, Iva and Schoots, Ivo and Caglic, Iztok and Zawaideh, Jeries P. and Wallström, Jonas and Bittencourt, Leonardo K. and Khurram, Misbah and Choi, Moon H. and Takahashi, Naoki and Tan, Nelly and Franco, Paolo N. and Gutierrez, Patricia A. and Thimansson, Per Erik and Hanus, Pieter and Puech, Philippe and Rau, Philipp R. and Visschere, Pieter de and Guillaume, Ramette and Cuocolo, Renato and Falcão, Ricardo O. and Stiphout, Rogier S. A. van and Girometti, Rossano and Briediene, Ruta and Grigienė, Rūta and Gitau, Samuel and Withey, Samuel and Ghai, Sangeet and Penzkofer, Tobias and Barrett, Tristan and Tammisetti, Varaha S. and Løgager, Vibeke B. and Černý, Vladimír and Venderink, Wulphert and Law, Yan M. and Lee, Young J. and Rooij, Maarten de and Huisman, Henkjan},
	year = {2024},
	pages = {879--887},
}

@misc{zeiler_visualizing_2013,
	title = {Visualizing and {Understanding} {Convolutional} {Networks}},
	url = {https://arxiv.org/abs/1311.2901},
	author = {Zeiler, Matthew D. and Fergus, Rob},
	year = {2013},
	note = {\_eprint: 1311.2901},
}

@misc{simonyan_deep_2014,
	title = {Deep {Inside} {Convolutional} {Networks}: {Visualising} {Image} {Classification} {Models} and {Saliency} {Maps}},
	url = {https://arxiv.org/abs/1312.6034},
	author = {Simonyan, Karen and Vedaldi, Andrea and Zisserman, Andrew},
	year = {2014},
	note = {\_eprint: 1312.6034},
}

@misc{kokhlikyan_captum_2020,
	title = {Captum: {A} unified and generic model interpretability library for {PyTorch}},
	url = {https://arxiv.org/abs/2009.07896},
	author = {Kokhlikyan, Narine and Miglani, Vivek and Martin, Miguel and Wang, Edward and Alsallakh, Bilal and Reynolds, Jonathan and Melnikov, Alexander and Kliushkina, Natalia and Araya, Carlos and Yan, Siqi and Reblitz-Richardson, Orion},
	year = {2020},
	note = {\_eprint: 2009.07896},
}

@misc{noauthor_image_nodate,
	title = {Image {Classification}},
	url = {https://paperswithcode.com/task/image-classification},
	urldate = {2023-02-20},
	note = {Publication Title: Papers With Code},
}

@misc{li_prostate_nodate,
	title = {The {Prostate} {Imaging}: {Cancer} {AI} ({PI}-{CAI}) 2022 {Grand} {Challenge} ({PIMed} {Team})},
        year = {2022},
	author = {Li, Xinran and Vesal, Sulaiman and Saunders, Sara and John, Simon and Soerensen, Christoph and Jahanandish, Hassan and Moroianu, Stefania and Bhattacharya, Indrani and Fan, Richard E and Sonn, Geoffrey A and {others}},
}

@misc{kan_implementation_nodate,
	title = {{IMPLEMENTATION} {METHOD} {OF} {THE} {PI}-{CAI} {CHALLENGE} ({SWANGEESE} {TEAM})},
	author = {Kan, Hongyu and Anhui, Hefei and Qiao, Liang and Shi, Jun and An, Hong},
        year = {2022}
}

@misc{noauthor_object_nodate,
	title = {Object {Detection}},
	url = {https://paperswithcode.com/task/object-detection},
	urldate = {2023-02-20},
	note = {Publication Title: Papers With Code},
}

@misc{noauthor_medical_nodate,
	title = {Medical {Image} {Segmentation} on {Synapse} multi-organ {CT}},
	url = {https://paperswithcode.com/sota/medical-image-segmentation-on-synapse-multi},
	urldate = {2023-02-20},
	note = {Publication Title: Papers With Code},
}

@article{futterer_can_2015,
	title = {Can clinically significant prostate cancer be detected with {Multiparametric} {Magnetic} {Resonance} {Imaging}? {A} systematic review of the literature},
	volume = {68},
	doi = {10.1016/j.eururo.2015.01.013},
	number = {6},
	journal = {European Urology},
	author = {Fütterer, Jurgen J. and Briganti, Alberto and De Visschere, Pieter and Emberton, Mark and Giannarini, Gianluca and Kirkham, Alex and Taneja, Samir S. and Thoeny, Harriet and Villeirs, Geert and Villers, Arnauld},
	year = {2015},
	pages = {1045--1053},
}

@article{loeb_systematic_2013,
	title = {Systematic review of complications of prostate biopsy},
	volume = {64},
	doi = {10.1016/j.eururo.2013.05.049},
	number = {6},
	journal = {European Urology},
	author = {Loeb, Stacy and Vellekoop, Annelies and Ahmed, Hashim U. and Catto, James and Emberton, Mark and Nam, Robert and Rosario, Derek J. and Scattoni, Vincenzo and Lotan, Yair},
	year = {2013},
	pages = {876--892},
}

@misc{gu_hippo_2020-1,
	title = {{HiPPO}: {Recurrent} {Memory} with {Optimal} {Polynomial} {Projections}},
	author = {Gu, Albert and Dao, Tri and Ermon, Stefano and Rudra, Atri and Re, Christopher},
	year = {2020},
	note = {\_eprint: 2008.07669},
}

@misc{liu_swin_2021,
	title = {Swin {Transformer}: {Hierarchical} {Vision} {Transformer} using {Shifted} {Windows}},
	copyright = {Creative Commons Attribution 4.0 International},
	url = {https://arxiv.org/abs/2103.14030},
	publisher = {arXiv},
	author = {Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
	year = {2021},
	doi = {10.48550/ARXIV.2103.14030},
	keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, Machine Learning (cs.LG)},
}

@article{dosovitskiy_image_2020,
	title = {An image is worth 16x16 words: {Transformers} for image recognition at scale},
	journal = {arXiv preprint arXiv:2010.11929},
	author = {Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and {others}},
	year = {2020},
}

@article{weinreb_pi-rads_2016,
	title = {{PI}-{Rads} {Prostate} {Imaging} – {Reporting} and {Data} {System}: 2015, version 2},
	volume = {69},
	doi = {10.1016/j.eururo.2015.08.052},
	number = {1},
	journal = {European Urology},
	author = {Weinreb, Jeffrey C. and Barentsz, Jelle O. and Choyke, Peter L. and Cornud, Francois and Haider, Masoom A. and Macura, Katarzyna J. and Margolis, Daniel and Schnall, Mitchell D. and Shtern, Faina and Tempany, Clare M. and al, et},
	year = {2016},
	pages = {16--40},
}

@article{borghesi_complications_2017-1,
	title = {Complications after systematic, random, and image-guided prostate biopsy},
	volume = {71},
	doi = {10.1016/j.eururo.2016.08.004},
	number = {3},
	journal = {European Urology},
	author = {Borghesi, Marco and Ahmed, Hashim and Nam, Robert and Schaeffer, Edward and Schiavina, Riccardo and Taneja, Samir and Weidner, Wolfgang and Loeb, Stacy},
	year = {2017},
	pages = {353--365},
}

@misc{saha_artificial_2022,
	title = {Artificial {Intelligence} and {Radiologists} at {Prostate} {Cancer} {Detection} in {MRI}: {The} {PI}-{CAI} {Challenge} ({Study} {Protocol})},
	doi = {10.5281/zenodo.6667655},
	author = {Saha, Anindo and Twilt, Jasper J. and Bosma, Joeran S. and Ginneken, Bram van and Yakar, Derya and Elschot, Mattijs and Veltman, Jeroen and Fütterer, Jurgen and Rooij, Maarten de and Huisman, Henkjan},
	year = {2022},
}

@article{caruana_multitask_1997,
	title = {Multitask {Learning}},
	volume = {28},
	doi = {10.1023/a:1007379606734},
	number = {1},
	journal = {Machine Learning},
	author = {Caruana, Rich},
	year = {1997},
	pages = {41--75},
}

@article{adams_prostate158_2022,
	title = {{PROSTATE158} - an expert-annotated {3T} {MRI} dataset and algorithm for prostate cancer detection},
	volume = {148},
	doi = {10.1016/j.compbiomed.2022.105817},
	journal = {Computers in Biology and Medicine},
	author = {Adams, Lisa C. and Makowski, Marcus R. and Engel, Günther and Rattunde, Maximilian and Busch, Felix and Asbach, Patrick and Niehues, Stefan M. and Vinayahalingam, Shankeeth and van Ginneken, Bram and Litjens, Geert and al, et},
	month = sep,
	year = {2022},
	pages = {105817},
}

@misc{ma_u-mamba_2024-1,
	title = {U-{Mamba}: {Enhancing} {Long}-range {Dependency} for {Biomedical} {Image} {Segmentation}},
	author = {Ma, Jun and Li, Feifei and Wang, Bo},
	year = {2024},
	note = {\_eprint: 2401.04722},
}

@misc{consortium_monai_2024,
	title = {{MONAI}: {Medical} {Open} {Network} for {AI}},
	url = {https://doi.org/10.5281/zenodo.7245505},
	publisher = {Zenodo},
	author = {Consortium, MONAI},
	month = may,
	year = {2024},
	doi = {10.5281/zenodo.7245505},
}

@article{loeb_systematic_2013-1,
	title = {Systematic review of complications of prostate biopsy},
	volume = {64},
	doi = {10.1016/j.eururo.2013.05.049},
	number = {6},
	journal = {European Urology},
	author = {Loeb, Stacy and Vellekoop, Annelies and Ahmed, Hashim U. and Catto, James and Emberton, Mark and Nam, Robert and Rosario, Derek J. and Scattoni, Vincenzo and Lotan, Yair},
	year = {2013},
	pages = {876--892},
}

@misc{hatamizadeh_swin_2022,
	title = {Swin {UNETR}: {Swin} {Transformers} for {Semantic} {Segmentation} of {Brain} {Tumors} in {MRI} {Images}},
	author = {Hatamizadeh, Ali and Nath, Vishwesh and Tang, Yucheng and Yang, Dong and Roth, Holger and Xu, Daguang},
	year = {2022},
	note = {\_eprint: 2201.01266},
}

@article{twilt_evaluating_2024,
	title = {Evaluating {Biparametric} {Versus} {Multiparametric} {Magnetic} {Resonance} {Imaging} for {Diagnosing} {Clinically} {Significant} {Prostate} {Cancer}: {An} {International}, {Paired}, {Noninferiority}, {Confirmatory} {Observer} {Study}},
	issn = {0302-2838},
	shorttitle = {Evaluating {Biparametric} {Versus} {Multiparametric} {Magnetic} {Resonance} {Imaging} for {Diagnosing} {Clinically} {Significant} {Prostate} {Cancer}},
	url = {https://www.sciencedirect.com/science/article/pii/S030228382402640X},
	doi = {10.1016/j.eururo.2024.09.035},
	abstract = {Background and objective
Biparametric magnetic resonance imaging (bpMRI), excluding dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI), is a potential replacement for multiparametric MRI (mpMRI) in diagnosing clinically significant prostate cancer (csPCa). An extensive international multireader multicase observer study was conducted to assess the noninferiority of bpMRI to mpMRI in csPCa diagnosis.
Methods
An observer study was conducted with 400 mpMRI examinations from four European centers, excluding examinations with prior prostate treatment or csPCa (Gleason grade [GG] ≥2) findings. Readers assessed bpMRI and mpMRI sequentially, assigning lesion-specific Prostate Imaging Reporting and Data System (PI-RADS) scores (3–5) and a patient-level suspicion score (0–100). The noninferiority of patient-level bpMRI versus mpMRI csPCa diagnosis was evaluated using the area under the receiver operating curve (AUROC) alongside the sensitivity and specificity at PI-RADS ≥3 with a 5\% margin. The secondary outcomes included insignificant prostate cancer (GG1) diagnosis, diagnostic evaluations at alternative risk thresholds, decision curve analyses (DCAs), and subgroup analyses considering reader expertise. Histopathology and ≥3 yr of follow-up were used for the reference standard.
Key findings and limitations
Sixty-two readers (45 centers and 20 countries) participated. The prevalence of csPCa was 33\% (133/400); bpMRI and mpMRI showed similar AUROC values of 0.853 (95\% confidence interval [CI], 0.819–0.887) and 0.859 (95\% CI, 0.826–0.893), respectively, with a noninferior difference of –0.6\% (95\% CI, –1.2\% to 0.1\%, p {\textless} 0.001). At PI-RADS ≥3, bpMRI and mpMRI had sensitivities of 88.6\% (95\% CI, 84.8–92.3\%) and 89.4\% (95\% CI, 85.8–93.1\%), respectively, with a noninferior difference of –0.9\% (95\% CI, –1.7\% to 0.0\%, p {\textless} 0.001), and specificities of 58.6\% (95\% CI, 52.3–63.1\%) and 57.7\% (95\% CI, 52.3–63.1\%), respectively, with a noninferior difference of 0.9\% (95\% CI, 0.0–1.8\%, p {\textless} 0.001). At alternative risk thresholds, mpMRI increased sensitivity at the expense of reduced specificity. DCA demonstrated the highest net benefit for an mpMRI pathway in cancer-averse scenarios, whereas a bpMRI pathway showed greater benefit for biopsy-averse scenarios. A subgroup analysis indicated limited additional benefit of DCE MRI for nonexperts. Limitations included that biopsies were conducted based on mpMRI imaging, and reading was performed in a sequential order.
Conclusions and clinical implications
It has been found that bpMRI is noninferior to mpMRI in csPCa diagnosis at AUROC, along with the sensitivity and specificity at PI-RADS ≥3, showing its value in individuals without prior csPCa findings and prostate treatment. Additional randomized prospective studies are required to investigate the generalizability of outcomes.},
	urldate = {2024-12-04},
	journal = {European Urology},
	author = {Twilt, Jasper J. and Saha, Anindo and Bosma, Joeran S. and van Ginneken, Bram and Bjartell, Anders and Padhani, Anwar R. and Bonekamp, David and Villeirs, Geert and Salomon, Georg and Giannarini, Gianluca and Kalpathy-Cramer, Jayashree and Barentsz, Jelle and Maier-Hein, Klaus H. and Rusu, Mirabela and Rouvière, Olivier and van den Bergh, Roderick and Panebianco, Valeria and Kasivisvanathan, Veeru and Obuchowski, Nancy A. and Yakar, Derya and Elschot, Mattijs and Veltman, Jeroen and Fütterer, Jurgen J. and Huisman, Henkjan and de Rooij, Maarten},
	month = oct,
	year = {2024},
	keywords = {Biparametric magnetic resonance imaging, Dynamic contrast-enhanced imaging, Multiparametric magnetic resonance imaging, Noninferiority, Observer study, Prostate cancer},
}

@article{yuan_z-ssmnet_2022,
	title = {Z-ssmnet: {A} zonal-aware self-supervised mesh network for prostate cancer detection and diagnosis in bpmri},
	journal = {arXiv preprint arXiv:2212.05808},
	author = {Yuan, Yuan and Ahn, Euijoon and Feng, Dagan and Khadra, Mohamad and Kim, Jinman},
	year = {2022},
}

@article{bosma_semisupervised_2023,
	title = {Semisupervised {Learning} with {Report}-guided {Pseudo} {Labels} for {Deep}                     {Learning}–based {Prostate} {Cancer} {Detection} {Using} {Biparametric}                     {MRI}},
	volume = {5},
	url = {https://pubs.rsna.org/doi/10.1148/ryai.230031},
	doi = {10.1148/ryai.230031},
	abstract = {Purpose To evaluate a novel method of semisupervised learning (SSL) guided by                             automated sparse information from diagnostic reports to leverage                             additional data for deep learning–based malignancy detection in                             patients with clinically significant prostate cancer.Materials and Methods This retrospective study included 7756 prostate MRI examinations (6380                             patients) performed between January 2014 and December 2020 for model                             development. An SSL method, report-guided SSL (RG-SSL), was developed                             for detection of clinically significant prostate cancer using                             biparametric MRI. RG-SSL, supervised learning (SL), and state-of-the-art                             SSL methods were trained using 100, 300, 1000, or 3050 manually                             annotated examinations. Performance on detection of clinically                             significant prostate cancer by RG-SSL, SL, and SSL was compared on 300                             unseen examinations from an external center with a histopathologically                             confirmed reference standard. Performance was evaluated using receiver                             operating characteristic (ROC) and free-response ROC analysis.                                 P values for performance differences were generated                             with a permutation test.Results At 100 manually annotated examinations, mean examination-based diagnostic                             area under the ROC curve (AUC) values for RG-SSL, SL, and the best SSL                             were 0.86 ± 0.01 (SD), 0.78 ± 0.03, and 0.81 ±                             0.02, respectively. Lesion-based detection partial AUCs were 0.62                             ± 0.02, 0.44 ± 0.04, and 0.48 ± 0.09, respectively.                             Examination-based performance of SL with 3050 examinations was matched                             by RG-SSL with 169 manually annotated examinations, thus requiring 14                             times fewer annotations. Lesion-based performance was matched with 431                             manually annotated examinations, requiring six times fewer                             annotations.Conclusion RG-SSL outperformed SSL in clinically significant prostate cancer                             detection and achieved performance similar to SL even at very low                             annotation budgets. Keywords: Annotation Efficiency, Computer-aided Detection                             and Diagnosis, MRI, Prostate Cancer, Semisupervised Deep Learning Supplemental material is available for this                                     article. Published under a CC BY 4.0 license.},
	number = {5},
	urldate = {2024-11-13},
	journal = {Radiology: Artificial Intelligence},
	author = {Bosma, Joeran                             S. and Saha, Anindo and Hosseinzadeh, Matin and Slootweg, Ivan and de                             Rooij, Maarten and Huisman, Henkjan},
	month = sep,
	year = {2023},
	note = {Publisher: Radiological Society of North America},
	pages = {e230031},
}

@misc{litjens_spie-aapm_2017,
	title = {{SPIE}-{AAPM} {PROSTATEx} {Challenge} {Data}},
	copyright = {Creative Commons Attribution 3.0 Unported},
	url = {https://www.cancerimagingarchive.net/collection/prostatex/},
	doi = {10.7937/K9TCIA.2017.MURS5CL},
	abstract = {SPIE, along with the support of the American Association of Physicists in Medicine (AAPM) and the National Cancer Institute (NCI), will conduct a “Grand Challenge” on quantitative image analysis methods for the diagnostic classification of clinically significant prostate lesions. As part of the 2017 SPIE Medical Imaging Symposium, the PROSTATEx Challenge will provide a unique opportunity for participants to compare their algorithms with those of others from academia, industry, and government in a structured, direct way using the same data sets. For more details, go to http://www.spie.org/PROSTATEx/},
	urldate = {2024-11-13},
	publisher = {The Cancer Imaging Archive},
	author = {Litjens, Geert and Debats, Oscar and Barentsz, Jelle and Karssemeijer, Nico and Huisman, Henkjan},
	collaborator = {{TCIA Team}},
	year = {2017},
}

@misc{pi-cai-dev-leaderboard,
  author = {Anindo Saha},
  title = {Open Development Phase - Tuning Leaderboard},
  year = 2025,
  url = {https://pi-cai.grand-challenge.org/evaluation/open-development-phase/leaderboard/},
  urldate = {2025-01-07}
}

@InProceedings{10.1007/978-3-031-72114-4_47,
author="Isensee, Fabian
and Wald, Tassilo
and Ulrich, Constantin
and Baumgartner, Michael
and Roy, Saikat
and Maier-Hein, Klaus
and J{\"a}ger, Paul F.",
editor="Linguraru, Marius George
and Dou, Qi
and Feragen, Aasa
and Giannarou, Stamatia
and Glocker, Ben
and Lekadir, Karim
and Schnabel, Julia A.",
title="nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024",
year="2024",
publisher="Springer Nature Switzerland",
address="Cham",
pages="488--498",
abstract="The release of nnU-Net marked a paradigm shift in 3D medical image segmentation, demonstrating that a properly configured U-Net architecture could still achieve state-of-the-art results. Despite this, the pursuit of novel architectures, and the respective claims of superior performance over the U-Net baseline, continued. In this study, we demonstrate that many of these recent claims fail to hold up when scrutinized for common validation shortcomings, such as the use of inadequate baselines, insufficient datasets, and neglected computational resources. By meticulously avoiding these pitfalls, we conduct a thorough and comprehensive benchmarking of current segmentation methods including CNN-based, Transformer-based, and Mamba-based approaches. In contrast to current beliefs, we find that the recipe for state-of-the-art performance is 1) employing CNN-based U-Net models, including ResNet and ConvNeXt variants, 2) using the nnU-Net framework, and 3) scaling models to modern hardware resources. These results indicate an ongoing innovation bias towards novel architectures in the field and underscore the need for more stringent validation standards in the quest for scientific progress.",
isbn="978-3-031-72114-4"
}


@article{zheng_atpca-net_2024,
	title = {{AtPCa}-Net: anatomical-aware prostate cancer detection network on multi-parametric {MRI}},
	volume = {14},
        year="2024",
	rights = {2024 The Author(s)},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-024-56405-7},
	doi = {10.1038/s41598-024-56405-7},
	shorttitle = {{AtPCa}-Net},
	abstract = {Multi-parametric {MRI} ({mpMRI}) is widely used for prostate cancer ({PCa}) diagnosis. Deep learning models show good performance in detecting {PCa} on {mpMRI}, but domain-specific {PCa}-related anatomical information is sometimes overlooked and not fully explored even by state-of-the-art deep learning models, causing potential suboptimal performances in {PCa} detection. Symmetric-related anatomical information is commonly used when distinguishing {PCa} lesions from other visually similar but benign prostate tissue. In addition, different combinations of {mpMRI} findings are used for evaluating the aggressiveness of {PCa} for abnormal findings allocated in different prostate zones. In this study, we investigate these domain-specific anatomical properties in {PCa} diagnosis and how we can adopt them into the deep learning framework to improve the model’s detection performance. We propose an anatomical-aware {PCa} detection Network ({AtPCa}-Net) for {PCa} detection on {mpMRI}. Experiments show that the {AtPCa}-Net can better utilize the anatomical-related information, and the proposed anatomical-aware designs help improve the overall model performance on both {PCa} detection and patient-level classification.},
	pages = {5740},
	number = {1},
	journal = {Scientific Reports},
	shortjournal = {Sci Rep},
	author = {Zheng, Haoxin and Hung, Alex Ling Yu and Miao, Qi and Song, Weinan and Scalzo, Fabien and Raman, Steven S. and Zhao, Kai and Sung, Kyunghyun},
	urldate = {2025-01-16},
	date = {2024-03-08},
	langid = {english},
	note = {Publisher: Nature Publishing Group},
	keywords = {Cancer imaging, Prostate, Urological cancer},
	file = {Full Text PDF:/Users/michaesl/Zotero/storage/3JDDTGMQ/Zheng et al. - 2024 - AtPCa-Net anatomical-aware prostate cancer detection network on multi-parametric MRI.pdf:application/pdf},
}

@article{Kou_2024,
doi = {10.1088/1361-6560/ad6ace},
url = {https://dx.doi.org/10.1088/1361-6560/ad6ace},
year = {2024},
month = {aug},
publisher = {IOP Publishing},
volume = {69},
number = {17},
pages = {175009},
author = {Kou, Weixuan and Marshall, Harry and Chiu, Bernard},
title = {Boundary-aware semantic clustering network for segmentation of prostate zones from T2-weighted MRI},
journal = {Physics in Medicine \& Biology},
abstract = {Objective. Automatic segmentation of prostatic zones from MRI can improve clinical diagnosis of prostate cancer as lesions in the peripheral zone (PZ) and central gland (CG) exhibit different characteristics. Existing approaches are limited in their accuracy in localizing the edges of PZ and CG. The proposed boundary-aware semantic clustering network (BASC-Net) improves segmentation performance by learning features in the vicinity of the prostate zonal boundaries, instead of only focusing on manually segmented boundaries. Approach. BASC-Net consists of two major components: the semantic clustering attention (SCA) module and the boundary-aware contrastive (BAC) loss. The SCA module implements a self-attention mechanism that extracts feature bases representing essential features of the inner body and boundary subregions and constructs attention maps highlighting each subregion. SCA is the first self-attention algorithm that utilizes ground truth masks to supervise the feature basis construction process. The features extracted from the inner body and boundary subregions of the same zone were integrated by BAC loss, which promotes the similarity of features extracted in the two subregions of the same zone. The BAC loss further promotes the difference between features extracted from different zones. Main results. BASC-Net was evaluated on the NCI-ISBI 2013 Challenge and Prostate158 datasets. An inter-dataset evaluation was conducted to evaluate the generalizability of the proposed method. BASC-Net outperformed nine state-of-the-art methods in all three experimental settings, attaining Dice similarity coefficients of 79.9% and 88.6% for PZ and CG, respectively, in the NCI-ISBI dataset, 80.5% and 89.2% for PZ and CG, respectively, in Prostate158 dataset, and 73.2% and 87.4% for PZ and CG, respectively, in the inter-dataset evaluation. Significance. As prostate lesions in PZ and CG have different characteristics, the zonal boundaries segmented by BASC-Net will facilitate prostate lesion detection.}
}


@article{cuocolo_deep_2021,
	title = {Deep Learning Whole-Gland and Zonal Prostate Segmentation on a Public {MRI} Dataset},
	volume = {54},
	issn = {1522-2586},
        year = {2021},
	doi = {10.1002/jmri.27585},
	pages = {452--459},
	number = {2},
	journal = {Journal of magnetic resonance imaging: {JMRI}},
	shortjournal = {J Magn Reson Imaging},
	author = {Cuocolo, Renato and Comelli, Albert and Stefano, Alessandro and Benfante, Viviana and Dahiya, Navdeep and Stanzione, Arnaldo and Castaldo, Anna and De Lucia, Davide Raffaele and Yezzi, Anthony and Imbriaco, Massimo},
	date = {2021-08},
	pmid = {33634932},
	keywords = {deep learning, Deep Learning, Humans, Image Processing, Computer-Assisted, machine learning, magnetic resonance imaging, Magnetic Resonance Imaging, Male, prostate, prostatic neoplasms, Prostatic Neoplasms, Retrospective Studies},
}


@article{aldoj_automatic_2020,
	title = {Automatic prostate and prostate zones segmentation of magnetic resonance images using {DenseNet}-like U-net},
	volume = {10},
	rights = {2020 The Author(s)},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-020-71080-0},
	doi = {10.1038/s41598-020-71080-0},
        year = {2020},
	pages = {14315},
	number = {1},
	journal = {Scientific Reports},
	shortjournal = {Sci Rep},
	author = {Aldoj, Nader and Biavati, Federico and Michallek, Florian and Stober, Sebastian and Dewey, Marc},
	urldate = {2025-01-16},
	date = {2020-08-31},
	langid = {english},
	note = {Publisher: Nature Publishing Group},
	keywords = {Biomedical engineering, Medical imaging},
	file = {Full Text PDF:/Users/michaesl/Zotero/storage/HZ25ZSP2/Aldoj et al. - 2020 - Automatic prostate and prostate zones segmentation of magnetic resonance images using DenseNet-like.pdf:application/pdf},
}



@article{zhao_multi-task_2023,
	title = {Multi-task deep learning for medical image computing and analysis: A review},
	volume = {153},
	issn = {0010-4825},
	url = {https://www.sciencedirect.com/science/article/pii/S0010482522012045},
	doi = {10.1016/j.compbiomed.2022.106496},
        year = {2023},
	shorttitle = {Multi-task deep learning for medical image computing and analysis},
	pages = {106496},
	journal = {Computers in Biology and Medicine},
	shortjournal = {Computers in Biology and Medicine},
	author = {Zhao, Yan and Wang, Xiuying and Che, Tongtong and Bao, Guoqing and Li, Shuyu},
	urldate = {2025-01-16},
	date = {2023-02-01},
	keywords = {Deep learning, Medical image analysis, Medical image application, Multi-task learning, Survey},
	file = {ScienceDirect Snapshot:/Users/michaesl/Zotero/storage/3T2C4EFA/S0010482522012045.html:text/html},
}


@article{kruger-stokke_multiparametric_2021,
	title = {Multiparametric Prostate {MRI} in Biopsy-Naïve Men: A Prospective Evaluation of Performance and Biopsy Strategies},
	volume = {11},
	issn = {2234-943X},
        year = {2021},
	doi = {10.3389/fonc.2021.745657},
	shorttitle = {Multiparametric Prostate {MRI} in Biopsy-Naïve Men},
	pages = {745657},
	journal = {Frontiers in Oncology},
	shortjournal = {Front Oncol},
	author = {Krüger-Stokke, Brage and Bertilsson, Helena and Langørgen, Sverre and Sjøbakk, Torill Anita Eidhammer and Bathen, Tone Frost and Selnæs, Kirsten Margrete},
	date = {2021},
	pmid = {34722302},
	pmcid = {PMC8552019},
	keywords = {cohort studies, image-guided biopsy, magnetic resonance imaging, prostate cancer, urologic diseases},
	file = {Full Text:/Users/michaesl/Zotero/storage/TG9C3TN7/Krüger-Stokke et al. - 2021 - Multiparametric Prostate MRI in Biopsy-Naïve Men A Prospective Evaluation of Performance and Biopsy.pdf:application/pdf},
}


@misc{jurdi_confidence_2023,
	title = {Confidence intervals for performance estimates in 3D medical image segmentation},
	url = {http://arxiv.org/abs/2307.10926},
	doi = {10.48550/arXiv.2307.10926},
	number = {{arXiv}:2307.10926},
        year = {2023},
	publisher = {{arXiv}},
	author = {Jurdi, R. El and Varoquaux, G. and Colliot, O.},
	urldate = {2025-01-20},
	date = {2023-07-21},
	eprinttype = {arxiv},
	eprint = {2307.10926 [eess]}
}


@article{racial_dce,
	title = {Racial Disparities in Quantitative {MRI} for African American and White Men with Prostate Cancer},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9949266/},
	doi = {10.21203/rs.3.rs-2547854/v1},
	pages = {rs.3.rs--2547854},
	journal = {Research Square},
	shortjournal = {Res Sq},
	author = {Zabihollahy, Fatemeh and Miao, Qi and Sonni, Ida and Vangala, Sitaram and Kim, Harrison and Hsu, William and Sisk, Anthony and Reiter, Robert and Raman, Steven and Sung, Kyunghyun},
	urldate = {2025-03-08},
        year= {2023},
	date = {2023-02-15},
	pmid = {36824946},
	pmcid = {PMC9949266},
	file = {PubMed Central Full Text PDF:/Users/michaesl/Zotero/storage/HAQBE6PW/Zabihollahy et al. - 2023 - Racial Disparities in Quantitative MRI for African American and White Men with Prostate Cancer.pdf:application/pdf},
}




