For: | Jang HJ, Lee A, Kang J, Song IH, Lee SH. Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning. World J Gastroenterol 2020; 26(40): 6207-6223 [PMID: 33177794 DOI: 10.3748/wjg.v26.i40.6207] |
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URL: | https://www.wjgnet.com/1007-9327/full/v26/i40/6207.htm |
Number | Citing Articles |
1 |
Mohammad Rizwan Alam, Kyung Jin Seo, Jamshid Abdul-Ghafar, Kwangil Yim, Sung Hak Lee, Hyun-Jong Jang, Chan Kwon Jung, Yosep Chong. Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancers. Briefings in Bioinformatics 2023; 24(3) doi: 10.1093/bib/bbad151
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2 |
Hyun-Jong Jang, Jai-Hyang Go, Younghoon Kim, Sung Hak Lee. Deep Learning for the Pathologic Diagnosis of Hepatocellular Carcinoma, Cholangiocarcinoma, and Metastatic Colorectal Cancer. Cancers 2023; 15(22): 5389 doi: 10.3390/cancers15225389
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3 |
Sung Hak Lee, In Hye Song, Hyun‐Jong Jang. Feasibility of deep learning‐based fully automated classification of microsatellite instability in tissue slides of colorectal cancer. International Journal of Cancer 2021; 149(3): 728 doi: 10.1002/ijc.33599
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4 |
Camilla Nero, Luca Boldrini, Jacopo Lenkowicz, Maria Teresa Giudice, Alessia Piermattei, Frediano Inzani, Tina Pasciuto, Angelo Minucci, Anna Fagotti, Gianfranco Zannoni, Vincenzo Valentini, Giovanni Scambia. Deep-Learning to Predict BRCA Mutation and Survival from Digital H&E Slides of Epithelial Ovarian Cancer. International Journal of Molecular Sciences 2022; 23(19): 11326 doi: 10.3390/ijms231911326
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5 |
Hyun-Jong Jang, In Hye Song, Sung Hak Lee. Generalizability of Deep Learning System for the Pathologic Diagnosis of Various Cancers. Applied Sciences 2021; 11(2): 808 doi: 10.3390/app11020808
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6 |
Theo Guitton, Pierre Allaume, Noémie Rabilloud, Nathalie Rioux-Leclercq, Sébastien Henno, Bruno Turlin, Marie-Dominique Galibert-Anne, Astrid Lièvre, Alexandra Lespagnol, Thierry Pécot, Solène-Florence Kammerer-Jacquet. Artificial Intelligence in Predicting Microsatellite Instability and KRAS, BRAF Mutations from Whole-Slide Images in Colorectal Cancer: A Systematic Review. Diagnostics 2023; 14(1): 99 doi: 10.3390/diagnostics14010099
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7 |
Amelie Echle, Narmin Ghaffari Laleh, Peter L. Schrammen, Nicholas P. West, Christian Trautwein, Titus J. Brinker, Stephen B. Gruber, Roman D. Buelow, Peter Boor, Heike I. Grabsch, Philip Quirke, Jakob N. Kather. Deep learning for the detection of microsatellite instability from histology images in colorectal cancer: A systematic literature review. ImmunoInformatics 2021; : 100008 doi: 10.1016/j.immuno.2021.100008
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8 |
Swati B. Bhonde, Sharmila K. Wagh, Jayashree R. Prasad. Identification of cancer types from gene expressions using learning techniques. Computer Methods in Biomechanics and Biomedical Engineering 2023; 26(16): 1951 doi: 10.1080/10255842.2022.2160243
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9 |
Yujie Jing, Chen Li, Tianming Du, Tao Jiang, Hongzan Sun, Jinzhu Yang, Liyu Shi, Minghe Gao, Marcin Grzegorzek, Xiaoyan Li. A comprehensive survey of intestine histopathological image analysis using machine vision approaches. Computers in Biology and Medicine 2023; 165: 107388 doi: 10.1016/j.compbiomed.2023.107388
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10 |
Yu Xiaojian, Qu Zhanbo, Chu Jian, Wang Zefeng, Liu Jian, Liu Jin, Pan Yuefen, Han Shuwen. Deep learning application in prediction of cancer molecular alterations based on pathological images: a bibliographic analysis via CiteSpace. Journal of Cancer Research and Clinical Oncology 2024; 150(10) doi: 10.1007/s00432-024-05992-z
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11 |
Neeraj Kumar, Ruchika Verma, Chuheng Chen, Cheng Lu, Pingfu Fu, Joseph Willis, Anant Madabhushi. Computer‐extracted features of nuclear morphology in hematoxylin and eosin images distinguish stage II and IV colon tumors. The Journal of Pathology 2022; 257(1): 17 doi: 10.1002/path.5864
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12 |
Linyan Chen, Hao Zeng, Yu Xiang, Yeqian Huang, Yuling Luo, Xuelei Ma. Histopathological Images and Multi-Omics Integration Predict Molecular Characteristics and Survival in Lung Adenocarcinoma. Frontiers in Cell and Developmental Biology 2021; 9 doi: 10.3389/fcell.2021.720110
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13 |
Sung Hak Lee, Yujin Lee, Hyun‐Jong Jang. Deep learning captures selective features for discrimination of microsatellite instability from pathologic tissue slides of gastric cancer. International Journal of Cancer 2023; 152(2): 298 doi: 10.1002/ijc.34251
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14 |
Ming Cai, Ke Zhao, Lin Wu, Yanqi Huang, Minning Zhao, Qingru Hu, Qicong Chen, Su Yao, Zhenhui Li, Xinjuan Fan, Zaiyi Liu. Artificial intelligence-based analysis of tumor-infiltrating lymphocyte spatial distribution for colorectal cancer prognosis. Chinese Medical Journal 2024; 137(4): 421 doi: 10.1097/CM9.0000000000002964
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15 |
Pierre Murchan, Cathal Ó’Brien, Shane O’Connell, Ciara S. McNevin, Anne-Marie Baird, Orla Sheils, Pilib Ó Broin, Stephen P. Finn. Deep Learning of Histopathological Features for the Prediction of Tumour Molecular Genetics. Diagnostics 2021; 11(8): 1406 doi: 10.3390/diagnostics11081406
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16 |
Sophia J. Wagner, Daniel Reisenbüchler, Nicholas P. West, Jan Moritz Niehues, Jiefu Zhu, Sebastian Foersch, Gregory Patrick Veldhuizen, Philip Quirke, Heike I. Grabsch, Piet A. van den Brandt, Gordon G.A. Hutchins, Susan D. Richman, Tanwei Yuan, Rupert Langer, Josien C.A. Jenniskens, Kelly Offermans, Wolfram Mueller, Richard Gray, Stephen B. Gruber, Joel K. Greenson, Gad Rennert, Joseph D. Bonner, Daniel Schmolze, Jitendra Jonnagaddala, Nicholas J. Hawkins, Robyn L. Ward, Dion Morton, Matthew Seymour, Laura Magill, Marta Nowak, Jennifer Hay, Viktor H. Koelzer, David N. Church, Christian Matek, Carol Geppert, Chaolong Peng, Cheng Zhi, Xiaoming Ouyang, Jacqueline A. James, Maurice B. Loughrey, Manuel Salto-Tellez, Hermann Brenner, Michael Hoffmeister, Daniel Truhn, Julia A. Schnabel, Melanie Boxberg, Tingying Peng, Jakob Nikolas Kather, David Church, Enric Domingo, Joanne Edwards, Bengt Glimelius, Ismail Gogenur, Andrea Harkin, Jen Hay, Timothy Iveson, Emma Jaeger, Caroline Kelly, Rachel Kerr, Noori Maka, Hannah Morgan, Karin Oien, Clare Orange, Claire Palles, Campbell Roxburgh, Owen Sansom, Mark Saunders, Ian Tomlinson. Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study. Cancer Cell 2023; 41(9): 1650 doi: 10.1016/j.ccell.2023.08.002
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17 |
Cuiqing Bai, Yan Sun, Xiuqin Zhang, Zhitong Zuo. Assessment of AURKA expression and prognosis prediction in lung adenocarcinoma using machine learning-based pathomics signature. Heliyon 2024; 10(12): e33107 doi: 10.1016/j.heliyon.2024.e33107
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18 |
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19 |
Matthew G. Hanna, Orly Ardon. Digital pathology systems enabling quality patient care. Genes, Chromosomes and Cancer 2023; 62(11): 685 doi: 10.1002/gcc.23192
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20 |
Xuejie Li, Xianda Chi, Pinjie Huang, Qiong Liang, Jianpei Liu. Deep neural network for the prediction of KRAS, NRAS, and BRAF genotypes in left-sided colorectal cancer based on histopathologic images. Computerized Medical Imaging and Graphics 2024; 115: 102384 doi: 10.1016/j.compmedimag.2024.102384
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21 |
Athena Davri, Effrosyni Birbas, Theofilos Kanavos, Georgios Ntritsos, Nikolaos Giannakeas, Alexandros T. Tzallas, Anna Batistatou. Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review. Diagnostics 2022; 12(4): 837 doi: 10.3390/diagnostics12040837
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22 |
Omer A.M. Saeed, Steven A. Mann, Claudio Luchini, Kun Huang, Shaobo Zhang, Joyashree D. Sen, Maria L. Piredda, Mingsheng Wang, Lee Ann Baldrige, R. Matthew Sperling, Kendra L. Curless, Liang Cheng. Evaluating mismatch repair deficiency for solid tumor immunotherapy eligibility: immunohistochemistry versus microsatellite molecular testing. Human Pathology 2021; 115: 10 doi: 10.1016/j.humpath.2021.05.009
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23 |
Pooria Mazaheri, Azam Asilian Bidgoli, Shahryar Rahnamayan, H.R. Tizhoosh. Ranking loss and sequestering learning for reducing image search bias in histopathology. Applied Soft Computing 2023; 142: 110346 doi: 10.1016/j.asoc.2023.110346
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24 |
Mai Tharwat, Nehal A. Sakr, Shaker El-Sappagh, Hassan Soliman, Kyung-Sup Kwak, Mohammed Elmogy. Colon Cancer Diagnosis Based on Machine Learning and Deep Learning: Modalities and Analysis Techniques. Sensors 2022; 22(23): 9250 doi: 10.3390/s22239250
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25 |
Vivek Kumar Singh, Yasmine Makhlouf, Md Mostafa Kamal Sarker, Stephanie Craig, Juvenal Baena, Christine Greene, Lee Mason, Jacqueline A James, Manuel Salto-Tellez, Paul O’Reilly, Perry Maxwell. KRASFormer: a fully vision transformer-based framework for predicting KRAS gene mutations in histopathological images of colorectal cancer. Biomedical Physics & Engineering Express 2024; 10(5): 055012 doi: 10.1088/2057-1976/ad5bed
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26 |
David L. Hölscher, Roman D. Bülow. Decoding pathology: the role of computational pathology in research and diagnostics. Pflügers Archiv - European Journal of Physiology 2024; doi: 10.1007/s00424-024-03002-2
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27 |
Sophia J. Wagner, Christian Matek, Sayedali Shetab Boushehri, Melanie Boxberg, Lorenz Lamm, Ario Sadafi, Dominik J.E. Winter, Carsten Marr, Tingying Peng. Built to Last? Reproducibility and Reusability of Deep Learning Algorithms in Computational Pathology. Modern Pathology 2024; 37(1): 100350 doi: 10.1016/j.modpat.2023.100350
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28 |
José Escorcia-Gutierrez, Margarita Gamarra, Paola Patricia Ariza-Colpas, Gisella Borja Roncallo, Nallig Leal, Roosvel Soto-Diaz, Romany F. Mansour. Galactic swarm optimization with deep transfer learning driven colorectal cancer classification for image guided intervention. Computers and Electrical Engineering 2022; 104: 108462 doi: 10.1016/j.compeleceng.2022.108462
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29 |
Louis-Oscar Morel, Valentin Derangère, Laurent Arnould, Sylvain Ladoire, Nathan Vinçon. Preliminary evaluation of deep learning for first-line diagnostic prediction of tumor mutational status. Scientific Reports 2023; 13(1) doi: 10.1038/s41598-023-34016-y
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30 |
Yulan Ma, Jiawen Wang, Kai Song, Yan Qiang, Xiong Jiao, Juanjuan Zhao. Spatial-Frequency dual-branch attention model for determining KRAS mutation status in colorectal cancer with T2-weighted MRI. Computer Methods and Programs in Biomedicine 2021; 209: 106311 doi: 10.1016/j.cmpb.2021.106311
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31 |
Hyun-Jong Jang, In-Hye Song, Sung-Hak Lee. Deep Learning for Automatic Subclassification of Gastric Carcinoma Using Whole-Slide Histopathology Images. Cancers 2021; 13(15): 3811 doi: 10.3390/cancers13153811
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32 |
Peter Schüffler, Katja Steiger, Wilko Weichert. How to use AI in pathology. Genes, Chromosomes and Cancer 2023; 62(9): 564 doi: 10.1002/gcc.23178
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33 |
Zhilong Lv, Rui Yan, Yuexiao Lin, Lin Gao, Fa Zhang, Ying Wang. A Disentangled Representation-Based Multimodal Fusion Framework Integrating Pathomics and Radiomics for KRAS Mutation Detection in Colorectal Cancer. Big Data Mining and Analytics 2024; 7(3): 590 doi: 10.26599/BDMA.2024.9020012
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34 |
Hardeep Kaur, Anil Kumar, Varinder Kaur Attri. Innovations in VLSI, Signal Processing and Computational Technologies. Lecture Notes in Electrical Engineering 2024; 1095: 395 doi: 10.1007/978-981-99-7077-3_39
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35 |
Pierre Murchan, Pilib Ó Broin, Anne-Marie Baird, Orla Sheils, Stephen P Finn. Deep feature batch correction using ComBat for machine learning applications in computational pathology. Journal of Pathology Informatics 2024; 15: 100396 doi: 10.1016/j.jpi.2024.100396
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36 |
Huu-Giao Nguyen, Oxana Lundström, Annika Blank, Heather Dawson, Alessandro Lugli, Maria Anisimova, Inti Zlobec. Image-based assessment of extracellular mucin-to-tumor area predicts consensus molecular subtypes (CMS) in colorectal cancer. Modern Pathology 2022; 35(2): 240 doi: 10.1038/s41379-021-00894-8
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37 |
Pei-Chen Tsai, Tsung-Hua Lee, Kun-Chi Kuo, Fang-Yi Su, Tsung-Lu Michael Lee, Eliana Marostica, Tomotaka Ugai, Melissa Zhao, Mai Chan Lau, Juha P. Väyrynen, Marios Giannakis, Yasutoshi Takashima, Seyed Mousavi Kahaki, Kana Wu, Mingyang Song, Jeffrey A. Meyerhardt, Andrew T. Chan, Jung-Hsien Chiang, Jonathan Nowak, Shuji Ogino, Kun-Hsing Yu. Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients. Nature Communications 2023; 14(1) doi: 10.1038/s41467-023-37179-4
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38 |
Jia-Sheng Cao, Zi-Yi Lu, Ming-Yu Chen, Bin Zhang, Sarun Juengpanich, Jia-Hao Hu, Shi-Jie Li, Win Topatana, Xue-Yin Zhou, Xu Feng, Ji-Liang Shen, Yu Liu, Xiu-Jun Cai. Artificial intelligence in gastroenterology and hepatology: Status and challenges. World Journal of Gastroenterology 2021; 27(16): 1664-1690 doi: 10.3748/wjg.v27.i16.1664
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39 |
Jan Moritz Niehues, Philip Quirke, Nicholas P. West, Heike I. Grabsch, Marko van Treeck, Yoni Schirris, Gregory P. Veldhuizen, Gordon G.A. Hutchins, Susan D. Richman, Sebastian Foersch, Titus J. Brinker, Junya Fukuoka, Andrey Bychkov, Wataru Uegami, Daniel Truhn, Hermann Brenner, Alexander Brobeil, Michael Hoffmeister, Jakob Nikolas Kather. Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study. Cell Reports Medicine 2023; 4(4): 100980 doi: 10.1016/j.xcrm.2023.100980
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40 |
Mamdouh M. Shawki, Mohamed Moustafa Azmy, Mohammed Salama, Sanaa Shawki. Mathematical and deep learning analysis based on tissue dielectric properties at low frequencies predict outcome in human breast cancer. Technology and Health Care 2022; 30(3): 633 doi: 10.3233/THC-213096
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41 |
Jana Lipkova, Richard J. Chen, Bowen Chen, Ming Y. Lu, Matteo Barbieri, Daniel Shao, Anurag J. Vaidya, Chengkuan Chen, Luoting Zhuang, Drew F.K. Williamson, Muhammad Shaban, Tiffany Y. Chen, Faisal Mahmood. Artificial intelligence for multimodal data integration in oncology. Cancer Cell 2022; 40(10): 1095 doi: 10.1016/j.ccell.2022.09.012
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42 |
Shuhei Ishii, Manabu Takamatsu, Hironori Ninomiya, Kentaro Inamura, Takeshi Horai, Akira Iyoda, Naoko Honma, Rira Hoshi, Yuko Sugiyama, Noriko Yanagitani, Mingyon Mun, Hitoshi Abe, Tetuo Mikami, Kengo Takeuchi. Machine learning‐based gene alteration prediction model for primary lung cancer using cytologic images. Cancer Cytopathology 2022; 130(10): 812 doi: 10.1002/cncy.22609
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43 |
Zengxin Liu, Caiwen Ma, Wenji She, Meilin Xie. Biomedical Image Segmentation Using Denoising Diffusion Probabilistic Models: A Comprehensive Review and Analysis. Applied Sciences 2024; 14(2): 632 doi: 10.3390/app14020632
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44 |
Hyun-Jong Jang, Ahwon Lee, Jun Kang, In Hye Song, Sung Hak Lee. Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach. World Journal of Gastroenterology 2021; 27(44): 7687-7704 doi: 10.3748/wjg.v27.i44.7687
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45 |
Qiuyan Sun, Tan Li, Zheng Wei, Zhiyi Ye, Xu Zhao, Jingjing Jing. Integrating transcriptomic data and digital pathology for NRG-based prediction of prognosis and therapy response in gastric cancer. Annals of Medicine 2024; 56(1) doi: 10.1080/07853890.2024.2426758
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46 |
Tobias Schulz, Christoph Becker, Gian Kayser. Ein Vergleich von 4 konvolutionalen neuronalen Netzen in der histopathologischen Diagnostik von Speicheldrüsenkarzinomen. HNO 2023; 71(3): 170 doi: 10.1007/s00106-023-01276-z
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47 |
Taher Dehkharghanian, Azam Asilian Bidgoli, Abtin Riasatian, Pooria Mazaheri, Clinton J. V. Campbell, Liron Pantanowitz, H. R. Tizhoosh, Shahryar Rahnamayan. Biased data, biased AI: deep networks predict the acquisition site of TCGA images. Diagnostic Pathology 2023; 18(1) doi: 10.1186/s13000-023-01355-3
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48 |
Sandra Orsulic, Joshi John, Ann E. Walts, Arkadiusz Gertych. Computational pathology in ovarian cancer. Frontiers in Oncology 2022; 12 doi: 10.3389/fonc.2022.924945
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49 |
Yeojin Jeong, Cristina Eunbee Cho, Ji-Eon Kim, Jonghyun Lee, Namkug Kim, Woon Yong Jung, Joohon Sung, Ju Han Kim, Yoo Jin Lee, Jiyoon Jung, Juyeon Pyo, Jisun Song, Jihwan Park, Kyoung Min Moon, Sangjeong Ahn. Deep learning model to predict Epstein–Barr virus associated gastric cancer in histology. Scientific Reports 2022; 12(1) doi: 10.1038/s41598-022-22731-x
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50 |
Didem Cifci, Sebastian Foersch, Jakob Nikolas Kather. Artificial intelligence to identify genetic alterations in conventional histopathology. The Journal of Pathology 2022; 257(4): 430 doi: 10.1002/path.5898
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51 |
Sarah Fremond, Viktor Hendrik Koelzer, Nanda Horeweg, Tjalling Bosse. The evolving role of morphology in endometrial cancer diagnostics: From histopathology and molecular testing towards integrative data analysis by deep learning. Frontiers in Oncology 2022; 12 doi: 10.3389/fonc.2022.928977
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52 |
Sung Hak Lee, Hyun-Jong Jang. Deep learning-based prediction of molecular cancer biomarkers from tissue slides: A new tool for precision oncology. Clinical and Molecular Hepatology 2022; 28(4): 754 doi: 10.3350/cmh.2021.0394
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53 |
Mohamed Tounsi, Donya Y. Abdulhussain, Ahmad Taher Azar, Ahmed Al-Khayyat, Ibraheem Kasim Ibraheem. Deep Learning Model-based Decision Support System for Kidney Cancer on Renal Images. Engineering, Technology & Applied Science Research 2024; 14(5): 17177 doi: 10.48084/etasr.8335
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