@misc{CDC_2022,
	title = {Basic {Information} {About} {Prostate} {Cancer}},
	url = {https://www.cdc.gov/cancer/prostate/basic\_info/index.htm},
	journal = {Centers for Disease Control and Prevention},
	author = {{Centers for Disease Control and Prevention}},
	month = oct,
	year = {2022},
}
@article{global_cancer_2024,
	title = {Global cancer statistics 2022: {GLOBOCAN} estimates of incidence and mortality worldwide for 36 cancers in 185 countries},
	volume = {74},
	issn = {0007-9235, 1542-4863},
	xurl ={https://acsjournals.onlinelibrary.wiley.com/doi/10.3322/caac.21834},
	doi = {10.3322/caac.21834},
	shorttitle = {Global cancer statistics 2022},
	pages = {229--263},
	number = {3},
	journaltitle = {{CA}: A Cancer Journal for Clinicians},
	shortjournal = {{CA} A Cancer J Clinicians},
	author = {Bray, Freddie and Laversanne, Mathieu and Sung, Hyuna and Ferlay, Jacques and Siegel, Rebecca L. and Soerjomataram, Isabelle and Jemal, Ahmedin},
	urldate = {2025-05-29},
	date = {2024-05},
	langid = {english}
}

@article{us_preventive_services_task_force_screening_2018,
	title = {Screening for {Prostate} {Cancer}: {US} {Preventive} {Services} {Task} {Force} {Recommendation} {Statement}},
	volume = {319},
	issn = {0098-7484},
	shorttitle = {Screening for {Prostate} {Cancer}},
	xurl ={http://jama.jamanetwork.com/article.aspx?doi=10.1001/jama.2018.3710},
	doi = {10.1001/jama.2018.3710},
	language = {en},
	number = {18},
	urldate = {2025-05-29},
	journal = {JAMA},
	author = {{US Preventive Services Task Force} and Grossman, David C. and Curry, Susan J. and Owens, Douglas K. and Bibbins-Domingo, Kirsten and Caughey, Aaron B. and Davidson, Karina W. and Doubeni, Chyke A. and Ebell, Mark and Epling, John W. and Kemper, Alex R. and Krist, Alex H. and Kubik, Martha and Landefeld, C. Seth and Mangione, Carol M. and Silverstein, Michael and Simon, Melissa A. and Siu, Albert L. and Tseng, Chien-Wen},
	month = may,
	year = {2018},
	pages = {1901},
}

@incollection{navab_u-net_2015,
	address = {Cham},
	title = {U-{Net}: {Convolutional} {Networks} for {Biomedical} {Image} {Segmentation}},
	volume = {9351},
	isbn = {978-3-319-24573-7 978-3-319-24574-4},
	shorttitle = {U-{Net}},
	xurl ={http://link.springer.com/10.1007/978-3-319-24574-4_28},
	language = {en},
	urldate = {2025-05-29},
	booktitle = {Medical {Image} {Computing} and {Computer}-{Assisted} {Intervention} – {MICCAI} 2015},
	publisher = {Springer International Publishing},
	author = {Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
	editor = {Navab, Nassir and Hornegger, Joachim and Wells, William M. and Frangi, Alejandro F.},
	year = {2015},
	doi = {10.1007/978-3-319-24574-4_28},
	note = {Series Title: Lecture Notes in Computer Science},
	pages = {234--241},
}

@article{naji_digital_2018,
	title = {Digital {Rectal} {Examination} for {Prostate} {Cancer} {Screening} in {Primary} {Care}: {A} {Systematic} {Review} and {Meta}-{Analysis}},
	volume = {16},
	issn = {1544-1709, 1544-1717},
	shorttitle = {Digital {Rectal} {Examination} for {Prostate} {Cancer} {Screening} in {Primary} {Care}},
	xurl ={http://www.annfammed.org/lookup/doi/10.1370/afm.2205},
	doi = {10.1370/afm.2205},
	language = {en},
	number = {2},
	urldate = {2025-05-29},
	journal = {The Annals of Family Medicine},
	author = {Naji, Leen and Randhawa, Harkanwal and Sohani, Zahra and Dennis, Brittany and Lautenbach, Deanna and Kavanagh, Owen and Bawor, Monica and Banfield, Laura and Profetto, Jason},
	month = mar,
	year = {2018},
	pages = {149--154},
}

@article{krilaviciute_digital_2023,
	title = {Digital {Rectal} {Examination} {Is} {Not} a {Useful} {Screening} {Test} for {Prostate} {Cancer}},
	volume = {6},
	issn = {25889311},
	xurl ={https://linkinghub.elsevier.com/retrieve/pii/S2588931123002031},
	doi = {10.1016/j.euo.2023.09.008},
	language = {en},
	number = {6},
	urldate = {2025-05-29},
	journal = {European Urology Oncology},
	author = {Krilaviciute, Agne and Becker, Nikolaus and Lakes, Jale and Radtke, Jan Philipp and Kuczyk, Markus and Peters, Inga and Harke, Nina N. and Debus, Jürgen and Koerber, Stefan A. and Herkommer, Kathleen and Gschwend, Jürgen E. and Meissner, Valentin H. and Benner, Axel and Seibold, Petra and Kristiansen, Glen and Hadaschik, Boris and Arsov, Christian and Schimmöller, Lars and Giesel, Frederik Lars and Antoch, Gerald and Makowski, Marcus and Wacker, Frank and Schlemmer, Heinz-Peter and Kaaks, Rudolf and Albers, Peter},
	month = dec,
	year = {2023},
	pages = {566--573},
}

@article{mcnally_biomarkers_2020,
	title = {Biomarkers {That} {Differentiate} {Benign} {Prostatic} {Hyperplasia} from {Prostate} {Cancer}: {A} {Literature} {Review}},
	volume = {Volume 12},
	copyright = {http://creativecommons.org/licenses/by-nc/3.0/},
	issn = {1179-1322},
	shorttitle = {Biomarkers {That} {Differentiate} {Benign} {Prostatic} {Hyperplasia} from {Prostate} {Cancer}},
	xurl ={https://www.dovepress.com/biomarkers-that-differentiate-benign-prostatic-hyperplasia-from-prosta-peer-reviewed-article-CMAR},
	doi = {10.2147/CMAR.S250829},
	language = {en},
	urldate = {2025-05-29},
	journal = {Cancer Management and Research},
	author = {McNally, Christopher J and Ruddock, Mark W and Moore, Tara and McKenna, Declan J},
	month = jul,
	year = {2020},
	pages = {5225--5241}
}

@article{moe_transrectal_2020,
	title = {Transrectal ultrasound biopsy of the prostate: does it still have a role in prostate cancer diagnosis?},
	volume = {9},
	issn = {22234683, 22234691},
	shorttitle = {Transrectal ultrasound biopsy of the prostate},
	xurl ={http://tau.amegroups.com/article/view/31253/html},
	doi = {10.21037/tau.2019.09.37},
	number = {6},
	urldate = {2025-05-29},
	journal = {Translational Andrology and Urology},
	author = {Moe, Andrew and Hayne, Dickon},
	month = dec,
	year = {2020},
	pages = {3018--3024},
}

@incollection{ibrahim_gadolinium_2025,
	address = {Treasure Island (FL)},
	title = {Gadolinium {Magnetic} {Resonance} {Imaging}},
	copyright = {Copyright © 2025, StatPearls Publishing LLC.},
	url ={http://www.ncbi.nlm.nih.gov/books/NBK482487/},
	language = {eng},
	urldate = {2025-05-29},
	booktitle = {{StatPearls}},
	publisher = {StatPearls Publishing},
	author = {Ibrahim, Michael A. and Hazhirkarzar, Bita and Dublin, Arthur B.},
	year = {2025},
	pmid = {29494094},
}

@article{choi_prebiopsy_2019,
	title = {Prebiopsy {Biparametric} {MRI} for {Clinically} {Significant} {Prostate} {Cancer} {Detection} {With} {PI}-{RADS} {Version} 2: {A} {Multicenter} {Study}},
	volume = {212},
	issn = {0361-803X, 1546-3141},
	shorttitle = {Prebiopsy {Biparametric} {MRI} for {Clinically} {Significant} {Prostate} {Cancer} {Detection} {With} {PI}-{RADS} {Version} 2},
	xurl ={https://www.ajronline.org/doi/10.2214/AJR.18.20498},
	doi = {10.2214/AJR.18.20498},
	language = {en},
	number = {4},
	urldate = {2025-05-29},
	journal = {American Journal of Roentgenology},
	author = {Choi, Moon Hyung and Kim, Chan Kyo and Lee, Young Joon and Jung, Seung Eun},
	month = apr,
	year = {2019},
	pages = {839--846},
}

@article{brembilla_diagnostic_2022,
	title = {Diagnostic {Accuracy} of {Abbreviated} {Bi}-{Parametric} {MRI} (a-{bpMRI}) for {Prostate} {Cancer} {Detection} and {Screening}: {A} {Multi}-{Reader} {Study}},
	volume = {12},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2075-4418},
	shorttitle = {Diagnostic {Accuracy} of {Abbreviated} {Bi}-{Parametric} {MRI} (a-{bpMRI}) for {Prostate} {Cancer} {Detection} and {Screening}},
	xurl ={https://www.mdpi.com/2075-4418/12/2/231},
	doi = {10.3390/diagnostics12020231},
	abstract = {(1) Background: There is currently limited evidence on the diagnostic accuracy of abbreviated biparametric MRI (a-bpMRI) protocols for prostate cancer (PCa) detection and screening. In the present study, we aim to investigate the performance of a-bpMRI among multiple readers and its potential application to an imaging-based screening setting. (2) Methods: A total of 151 men who underwent 3T multiparametric MRI (mpMRI) of the prostate and transperineal template prostate mapping biopsies were retrospectively selected. Corresponding bpMRI (multiplanar T2WI, DWI, ADC maps) and a-bpMRI (axial T2WI and b 2000 s/mm2 DWI only) dataset were derived from mpMRI. Three experienced radiologists scored a-bpMRI, standard biparametric MRI (bpMRI) and mpMRI in separate sessions. Diagnostic accuracy and interreader agreement of a-bpMRI was tested for different positivity thresholds and compared to bpMRI and mpMRI. Predictive values of a-bpMRI were computed for lower levels of PCa prevalence to simulate a screening setting. The primary definition of clinically significant PCa (csPCa) was Gleason ≥ 4 + 3, or cancer core length ≥ 6 mm. (3) Results: The median age was 62 years, the median PSA was 6.8 ng/mL, and the csPCa prevalence was 40\%. Using a cut off of MRI score ≥ 3, the sensitivity and specificity of a-bpMRI were 92\% and 48\%, respectively. There was no significant difference in sensitivity compared to bpMRI and mpMRI. Interreader agreement of a-bpMRI was moderate (AC1 0.58). For a low prevalence of csPCa (e.g., {\textless}10\%), higher cut offs (MRI score ≥ 4) yield a more favourable balance between the predictive values and positivity rate of MRI. (4) Conclusion: Abbreviated bpMRI protocols could match the diagnostic accuracy of bpMRI and mpMRI for the detection of csPCa. If a-bpMRI is used in low-prevalence settings, higher cut-offs for MRI positivity should be prioritised.},
	language = {en},
	number = {2},
	urldate = {2025-05-29},
	journal = {Diagnostics},
	author = {Brembilla, Giorgio and Giganti, Francesco and Sidhu, Harbir and Imbriaco, Massimo and Mallett, Sue and Stabile, Armando and Freeman, Alex and Ahmed, Hashim U. and Moore, Caroline and Emberton, Mark and Punwani, Shonit},
	month = jan,
	year = {2022},
	pages = {231},
}

@article{xu_comparison_2019,
	title = {Comparison of biparametric and multiparametric {MRI} in the diagnosis of prostate cancer},
	volume = {19},
	issn = {1470-7330},
	xurl ={https://cancerimagingjournal.biomedcentral.com/articles/10.1186/s40644-019-0274-9},
	doi = {10.1186/s40644-019-0274-9},
	abstract = {Abstract
            
              Purpose
              To compare the diagnostic accuracy of biparametric MRI (bpMRI) and multiparametric MRI (mpMRI) for prostate cancer (PCa) and clinically significant prostate cancer (csPCa) and to explore the application value of dynamic contrast-enhanced (DCE) MRI in prostate imaging.
            
            
              Methods and materials
              This study retrospectively enrolled 235 patients with suspected PCa in our hospital from January 2016 to December 2017, and all lesions were histopathologically confirmed. The lesions were scored according to the Prostate Imaging Reporting and Data System version 2 (PI-RADS V2). The bpMRI (T2-weighted imaging [T2WI], diffusion-weighted imaging [DWI]/apparent diffusion coefficient [ADC]) and mpMRI (T2WI, DWI/ADC and DCE) scores were recorded to plot the receiver operating characteristic (ROC) curves. The area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) for each method were calculated and compared. The patients were further stratified according to bpMRI scores (bpMRI ≥3, and bpMRI = 3, 4, 5) to analyse the difference in DCE MRI between PCa and non-PCa lesions (as well as between csPCa and non-csPCa).
            
            
              Results
              
                The AUC values for the bpMRI and mpMRI protocols for PCa were comparable (0.790 [0.732–0.840] and 0.791 [0.733–0.841], respectively). The accuracy, sensitivity, specificity, PPV and NPV of bpMRI for PCa were 76.2, 79.5, 72.6, 75.8, and 76.6\%, respectively, and the values for mpMRI were 77.4, 84.4, 69.9, 75.2, and 80.6\%, respectively. The AUC values for the bpMRI and mpMRI protocols for the diagnosis of csPCa were similar (0.781 [0.722–0.832] and 0.779 [0.721–0.831], respectively). The accuracy, sensitivity, specificity, PPV and NPV of bpMRI for csPCa were 74.0, 83.8, 66.9, 64.8, and 85.0\%, respectively; and 73.6, 87.9, 63.2, 63.2, and 87.8\%, respectively, for mpMRI. For patients with bpMRI scores ≥3, positive DCE results were more common in PCa and csPCa lesions (both
                P
                 = 0.001). Further stratification analysis showed that for patients with a bpMRI score = 4, PCa and csPCa lesions were more likely to have positive DCE results (
                P
                 = 0.003 and
                P
                 {\textless} 0.001, respectively).
              
            
            
              Conclusion
              The diagnostic accuracy of bpMRI is comparable with that of mpMRI in the detection of PCa and the identification of csPCa. DCE MRI is helpful in further identifying PCa and csPCa lesions in patients with bpMRI ≥3, especially bpMRI = 4, which may be conducive to achieving a more accurate PCa risk stratification. Rather than omitting DCE, we think further comprehensive studies are required for prostate MRI.},
	language = {en},
	number = {1},
	urldate = {2025-05-29},
	journal = {Cancer Imaging},
	author = {Xu, Lili and Zhang, Gumuyang and Shi, Bing and Liu, Yanhan and Zou, Tingting and Yan, Weigang and Xiao, Yu and Xue, Huadan and Feng, Feng and Lei, Jing and Jin, Zhengyu and Sun, Hao},
	month = dec,
	year = {2019},
	pages = {90},
}

@article{noh_comparison_2022,
	title = {Comparison between biparametric and multiparametric {MRI} in predicting muscle invasion by bladder cancer based on the {VI}-{RADS}},
	volume = {12},
	issn = {2045-2322},
	xurl ={https://www.nature.com/articles/s41598-022-19273-7},
	doi = {10.1038/s41598-022-19273-7},
	abstract = {Abstract
            This study aimed to compare the diagnostic validity of biparametric magnetic resonance imaging (bpMRI) with that of multiparametric MRI (mpMRI) based on the Vesicle Imaging-Reporting and Data System (VI-RADS) in predicting muscle invasion by bladder cancer (BCa). We retrospectively examined 357 patients with an initial diagnosis of BCa who underwent preoperative MRI; 257 and 100 patients underwent mpMRI and bpMRI, respectively. Two urogenital radiologists evaluated all bpMRI and mpMRI scans using VI-RADS, and the diagnostic validity of VI-RADS for predicting muscle invasion by BCa was analyzed based on histopathology of the first and/or second transurethral resection of bladder tumors and radical cystectomy. Receiver operating characteristic (ROC) curves were plotted with the calculation of area under the curves (AUCs), and the level of significance was P {\textless} 0.05. Both groups showed optimal performance with a VI-RADS score ≥ 3. BpMRI showed comparable diagnostic performance to mpMRI (reader 1: AUC, 0.903 [0.827–0.954] vs. 0.935 [0.884–0.968], p = 0.510; and reader 2: AUC, 0.901 [0.814–0.945] vs. 0.915 [0.874–0.946]; p = 0.655). The inter-reader agreement between both readers was excellent (Cohen’s kappa value = 0.942 and 0.905 for bpMRI and mpMRI, respectively). This comparative study suggests that bpMRI has comparable diagnostic performance to mpMRI and may be an alternative option to predict muscle invasion by BCa.},
	language = {en},
	number = {1},
	urldate = {2025-05-29},
	journal = {Scientific Reports},
	author = {Noh, Tae Il and Shim, Ji Sung and Kang, Sung Gu and Sung, Deuk Jae and Cheon, Jun and Sim, Ki Choon and Kang, Seok Ho},
	month = nov,
	year = {2022},
	pages = {20689},
}

@article{bell_sharing_2024,
	title = {Sharing {Data} {Is} {Essential} for the {Future} of {AI} in {Medical} {Imaging}},
	volume = {6},
	issn = {2638-6100},
	xurl ={http://pubs.rsna.org/doi/10.1148/ryai.230337},
	doi = {10.1148/ryai.230337},
	language = {en},
	number = {1},
	urldate = {2025-05-29},
	journal = {Radiology: Artificial Intelligence},
	author = {Bell, Laura C. and Shimron, Efrat},
	month = jan,
	year = {2024},
	pages = {e230337},
}

@article{tibrewala_fastmri_2024,
	title = {{FastMRI} {Prostate}: {A} public, biparametric {MRI} dataset to advance machine learning for prostate cancer imaging},
	volume = {11},
	issn = {2052-4463},
	shorttitle = {{FastMRI} {Prostate}},
	xurl ={https://www.nature.com/articles/s41597-024-03252-w},
	doi = {10.1038/s41597-024-03252-w},
	abstract = {Abstract
            Magnetic resonance imaging (MRI) has experienced remarkable advancements in the integration of artificial intelligence (AI) for image acquisition and reconstruction. The availability of raw k-space data is crucial for training AI models in such tasks, but public MRI datasets are mostly restricted to DICOM images only. To address this limitation, the fastMRI initiative released brain and knee k-space datasets, which have since seen vigorous use. In May 2023, fastMRI was expanded to include biparametric (T2- and diffusion-weighted) prostate MRI data from a clinical population. Biparametric MRI plays a vital role in the diagnosis and management of prostate cancer. Advances in imaging methods, such as reconstructing under-sampled data from accelerated acquisitions, can improve cost-effectiveness and accessibility of prostate MRI. Raw k-space data, reconstructed images and slice, volume and exam level annotations for likelihood of prostate cancer are provided in this dataset for 47468 slices corresponding to 1560 volumes from 312 patients. This dataset facilitates AI and algorithm development for prostate image reconstruction, with the ultimate goal of enhancing prostate cancer diagnosis.},
	language = {en},
	number = {1},
	urldate = {2025-05-29},
	journal = {Scientific Data},
	author = {Tibrewala, Radhika and Dutt, Tarun and Tong, Angela and Ginocchio, Luke and Lattanzi, Riccardo and Keerthivasan, Mahesh B. and Baete, Steven H. and Chopra, Sumit and Lui, Yvonne W. and Sodickson, Daniel K. and Chandarana, Hersh and Johnson, Patricia M.},
	month = apr,
	year = {2024},
	pages = {404},
}

@inproceedings{huang_densely_2017,
	address = {Honolulu, HI},
	title = {Densely {Connected} {Convolutional} {Networks}},
	isbn = {978-1-5386-0457-1},
	xurl ={https://ieeexplore.ieee.org/document/8099726/},
	doi = {10.1109/CVPR.2017.243},
	urldate = {2025-05-29},
	booktitle = {2017 {IEEE} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})},
	publisher = {IEEE},
	author = {Huang, Gao and Liu, Zhuang and Van Der Maaten, Laurens and Weinberger, Kilian Q.},
	month = jul,
	year = {2017},
	pages = {2261--2269},
}

@inproceedings{he_deep_2016,
	address = {Las Vegas, NV, USA},
	title = {Deep {Residual} {Learning} for {Image} {Recognition}},
	isbn = {978-1-4673-8851-1},
	xurl ={http://ieeexplore.ieee.org/document/7780459/},
	doi = {10.1109/CVPR.2016.90},
	urldate = {2025-05-29},
	booktitle = {2016 {IEEE} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})},
	publisher = {IEEE},
	author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
	month = jun,
	year = {2016},
	pages = {770--778},
}

@misc{oquab2023dinov2,
  title={DINOv2: Learning Robust Visual Features without Supervision},
  author={Oquab, Maxime and Darcet, Timothée and Moutakanni, Theo and Vo, Huy V. and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and Howes, Russell and Huang, Po-Yao and Xu, Hu and Sharma, Vasu and Li, Shang-Wen and Galuba, Wojciech and Rabbat, Mike and Assran, Mido and Ballas, Nicolas and Synnaeve, Gabriel and Misra, Ishan and Jegou, Herve and Mairal, Julien and Labatut, Patrick and Joulin, Armand and Bojanowski, Piotr},
  journal={arXiv:2304.07193},
  year={2023}
}

@InProceedings{vgg_simonyan,
  author       = "Karen Simonyan and Andrew Zisserman",
  title        = "Very Deep Convolutional Networks for Large-Scale Image Recognition",
  booktitle    = "International Conference on Learning Representations",
  year         = "2015",
}

@inbook{pytorch,
author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and K\"{o}pf, Andreas and Yang, Edward and DeVito, Zach and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
title = {PyTorch: an imperative style, high-performance deep learning library},
year = {2019},
publisher = {Curran Associates Inc.},
address = {Red Hook, NY, USA},
abstract = {Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it provides an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs.In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several common benchmarks.},
booktitle = {Proceedings of the 33rd International Conference on Neural Information Processing Systems},
articleno = {721},
numpages = {12}
}


@article{scott_pi-rads_2021,
	title = {{PI}-{RADS} v2.1: {What} has changed and how to report},
	volume = {25},
	copyright = {https://creativecommons.org/licenses/by/4.0},
	issn = {2078-6778, 1027-202X},
	shorttitle = {{PI}-{RADS} v2.1},
	xurl ={http://www.sajr.org.za/index.php/SAJR/article/view/2062},
	doi = {10.4102/sajr.v25i1.2062},
	language = {en},
	number = {1},
	urldate = {2025-05-29},
	journal = {South African Journal of Radiology},
	author = {Scott, Robin and Misser, Shalendra K. and Cioni, Dania and Neri, Emanuele},
	month = jun,
	year = {2021},
	file = {Full Text:/Users/shebna/Zotero/storage/UIF44YAV/Scott et al. - 2021 - PI-RADS v2.1 What has changed and how to report.pdf:application/pdf},
}

@INPROCEEDINGS {huang_dinov2,
    author = { Huang, Yuning and Zou, Jingchen and Meng, Lanxi and Yue, Xin and Zhao, Qing and Li, Jianqiang and Song, Changwei and Jimenez, Gabriel and Li, Shaowu and Fu, Guanghui },
    booktitle = { 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC) },
    title = {{ Comparative Analysis of ImageNet Pre-Trained Deep Learning Models and DINOv2 in Medical Imaging Classification }},
    year = {2024},
    volume = {},
    ISSN = {},
    pages = {297-305},
    abstract = { Medical image analysis frequently encounters data scarcity challenges. Transfer learning has been effective in addressing this issue while conserving computational resources. The recent advent of foundational models like the DINOv2, which uses the vision transformer architecture, has opened new opportunities in the field and gathered significant interest. However, DINOv2's performance on clinical data still needs to be verified. In this paper, we performed a glioma grading task using three clinical modalities of brain MRI data. We compared the performance of various pre-trained deep learning models, including those based on ImageNet and DINOv2, in a transfer learning context. Our focus was on understanding the impact of the freezing mechanism on performance. We also validated our findings on three other types of public datasets: chest radiography, fundus radiography, and dermoscopy. Our findings indicate that in our clinical dataset, DINOv2's performance was not as strong as ImageNet-based pre-trained models, whereas in public datasets, DINOv2 generally outperformed other models, especially when using the frozen mechanism. Similar performance was observed with various sizes of DINOv2 models across different tasks. In summary, DINOv2 is viable for medical image classification tasks, particularly with data resembling natural images. However, its effectiveness may vary with data that significantly differs from natural images such as MRI. In addition, employing smaller versions of the model can be adequate for medical task, offering resource-saving benefits. Our codes are available at https://github.com/GuanghuiFU/medical_dino_eval. },
    keywords = {Deep learning;Training;Radiography;Adaptation models;Computational modeling;Transfer learning;Brain modeling},
    doi = {10.1109/COMPSAC61105.2024.00049},
    xurl ={https://doi.ieeecomputersociety.org/10.1109/COMPSAC61105.2024.00049},
    publisher = {IEEE Computer Society},
    address = {Los Alamitos, CA, USA},
    month =Jul
}

@Article{liu2022convnet,
  author  = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
  title   = {A ConvNet for the 2020s},
  journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year    = {2022},
}

@INPROCEEDINGS{ImagenetDeng2009,
  author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Kai Li and Li Fei-Fei},
  booktitle={2009 IEEE Conference on Computer Vision and Pattern Recognition}, 
  title={ImageNet: A large-scale hierarchical image database}, 
  year={2009},
  volume={},
  number={},
  pages={248-255},
  keywords={Large-scale systems;Image databases;Explosions;Internet;Robustness;Information retrieval;Image retrieval;Multimedia databases;Ontologies;Spine},
  doi={10.1109/CVPR.2009.5206848}}

