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For: Hägele M, Seegerer P, Lapuschkin S, Bockmayr M, Samek W, Klauschen F, Müller KR, Binder A. Resolving challenges in deep learning-based analyses of histopathological images using explanation methods. Sci Rep 2020;10:6423. [PMID: 32286358 DOI: 10.1038/s41598-020-62724-2] [Cited by in Crossref: 25] [Cited by in F6Publishing: 15] [Article Influence: 12.5] [Reference Citation Analysis]
Number Citing Articles
1 Anders CJ, Weber L, Neumann D, Samek W, Müller K, Lapuschkin S. Finding and removing Clever Hans: Using explanation methods to debug and improve deep models. Information Fusion 2022;77:261-95. [DOI: 10.1016/j.inffus.2021.07.015] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
2 Sun J, Lapuschkin S, Samek W, Binder A. Explain and improve: LRP-inference fine-tuning for image captioning models. Information Fusion 2022;77:233-46. [DOI: 10.1016/j.inffus.2021.07.008] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
3 Ash JT, Darnell G, Munro D, Engelhardt BE. Joint analysis of expression levels and histological images identifies genes associated with tissue morphology. Nat Commun 2021;12:1609. [PMID: 33707455 DOI: 10.1038/s41467-021-21727-x] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
4 Ali M, Ali R. Multi-Input Dual-Stream Capsule Network for Improved Lung and Colon Cancer Classification. Diagnostics (Basel) 2021;11:1485. [PMID: 34441419 DOI: 10.3390/diagnostics11081485] [Reference Citation Analysis]
5 Tian L, Hunt B, Bell MAL, Yi J, Smith JT, Ochoa M, Intes X, Durr NJ. Deep Learning in Biomedical Optics. Lasers Surg Med 2021;53:748-75. [PMID: 34015146 DOI: 10.1002/lsm.23414] [Reference Citation Analysis]
6 Capobianco E, Deng J. Radiomics at a Glance: A Few Lessons Learned from Learning Approaches. Cancers (Basel) 2020;12:E2453. [PMID: 32872466 DOI: 10.3390/cancers12092453] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
7 Rao RS, Shivanna DB, Mahadevpur KS, Shivaramegowda SG, Prakash S, Lakshminarayana S, Patil S. Deep Learning-Based Microscopic Diagnosis of Odontogenic Keratocysts and Non-Keratocysts in Haematoxylin and Eosin-Stained Incisional Biopsies. Diagnostics 2021;11:2184. [DOI: 10.3390/diagnostics11122184] [Reference Citation Analysis]
8 Güllmar D, Jacobsen N, Deistung A, Timmann D, Ropele S, Reichenbach JR. Investigation of biases in convolutional neural networks for semantic segmentation using performance sensitivity analysis. Z Med Phys 2022:S0939-3889(21)00109-4. [PMID: 35016819 DOI: 10.1016/j.zemedi.2021.11.004] [Reference Citation Analysis]
9 Melanthota SK, Gopal D, Chakrabarti S, Kashyap AA, Radhakrishnan R, Mazumder N. Deep learning-based image processing in optical microscopy. Biophys Rev. [DOI: 10.1007/s12551-022-00949-3] [Reference Citation Analysis]
10 Ahmad F, Mahmood A, Muhmood T. Machine learning-integrated omics for the risk and safety assessment of nanomaterials. Biomater Sci 2021;9:1598-608. [PMID: 33443512 DOI: 10.1039/d0bm01672a] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
11 Bussola N, Papa B, Melaiu O, Castellano A, Fruci D, Jurman G. Quantification of the Immune Content in Neuroblastoma: Deep Learning and Topological Data Analysis in Digital Pathology. Int J Mol Sci 2021;22:8804. [PMID: 34445517 DOI: 10.3390/ijms22168804] [Reference Citation Analysis]
12 Oyelade ON, Ezugwu AE. A bioinspired neural architecture search based convolutional neural network for breast cancer detection using histopathology images. Sci Rep 2021;11:19940. [PMID: 34620891 DOI: 10.1038/s41598-021-98978-7] [Reference Citation Analysis]
13 Khened M, Kori A, Rajkumar H, Krishnamurthi G, Srinivasan B. A generalized deep learning framework for whole-slide image segmentation and analysis. Sci Rep 2021;11:11579. [PMID: 34078928 DOI: 10.1038/s41598-021-90444-8] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Borrellas P, Unceta I. The Challenges of Machine Learning and Their Economic Implications. Entropy (Basel) 2021;23:275. [PMID: 33668772 DOI: 10.3390/e23030275] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 Levy-Jurgenson A, Tekpli X, Kristensen VN, Yakhini Z. Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer. Sci Rep 2020;10:18802. [PMID: 33139755 DOI: 10.1038/s41598-020-75708-z] [Cited by in Crossref: 9] [Cited by in F6Publishing: 6] [Article Influence: 4.5] [Reference Citation Analysis]
16 Hauser K, Kurz A, Haggenmüller S, Maron RC, von Kalle C, Utikal JS, Meier F, Hobelsberger S, Gellrich FF, Sergon M, Hauschild A, French LE, Heinzerling L, Schlager JG, Ghoreschi K, Schlaak M, Hilke FJ, Poch G, Kutzner H, Berking C, Heppt MV, Erdmann M, Haferkamp S, Schadendorf D, Sondermann W, Goebeler M, Schilling B, Kather JN, Fröhling S, Lipka DB, Hekler A, Krieghoff-Henning E, Brinker TJ. Explainable artificial intelligence in skin cancer recognition: A systematic review. Eur J Cancer 2022;167:54-69. [PMID: 35390650 DOI: 10.1016/j.ejca.2022.02.025] [Reference Citation Analysis]
17 Solari EL, Gafita A, Schachoff S, Bogdanović B, Villagrán Asiares A, Amiel T, Hui W, Rauscher I, Visvikis D, Maurer T, Schwamborn K, Mustafa M, Weber W, Navab N, Eiber M, Hatt M, Nekolla SG. The added value of PSMA PET/MR radiomics for prostate cancer staging. Eur J Nucl Med Mol Imaging 2021. [PMID: 34255130 DOI: 10.1007/s00259-021-05430-z] [Reference Citation Analysis]
18 Sohail A, Khan A, Wahab N, Zameer A, Khan S. A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images. Sci Rep 2021;11:6215. [PMID: 33737632 DOI: 10.1038/s41598-021-85652-1] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 5.0] [Reference Citation Analysis]
19 van der Velden BH, Kuijf HJ, Gilhuijs KG, Viergever MA. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Medical Image Analysis 2022;79:102470. [DOI: 10.1016/j.media.2022.102470] [Reference Citation Analysis]
20 Xiong P, Lee SM, Chan G. Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review. Front Cardiovasc Med 2022;9:860032. [DOI: 10.3389/fcvm.2022.860032] [Reference Citation Analysis]
21 Pati P, Jaume G, Foncubierta-Rodríguez A, Feroce F, Anniciello AM, Scognamiglio G, Brancati N, Fiche M, Dubruc E, Riccio D, Di Bonito M, De Pietro G, Botti G, Thiran JP, Frucci M, Goksel O, Gabrani M. Hierarchical graph representations in digital pathology. Med Image Anal 2021;75:102264. [PMID: 34781160 DOI: 10.1016/j.media.2021.102264] [Reference Citation Analysis]
22 Oala L, Murchison AG, Balachandran P, Choudhary S, Fehr J, Leite AW, Goldschmidt PG, Johner C, Schörverth EDM, Nakasi R, Meyer M, Cabitza F, Baird P, Prabhu C, Weicken E, Liu X, Wenzel M, Vogler S, Akogo D, Alsalamah S, Kazim E, Koshiyama A, Piechottka S, Macpherson S, Shadforth I, Geierhofer R, Matek C, Krois J, Sanguinetti B, Arentz M, Bielik P, Calderon-Ramirez S, Abbood A, Langer N, Haufe S, Kherif F, Pujari S, Samek W, Wiegand T. Machine Learning for Health: Algorithm Auditing & Quality Control. J Med Syst 2021;45:105. [PMID: 34729675 DOI: 10.1007/s10916-021-01783-y] [Reference Citation Analysis]
23 Kuklyte J, Fitzgerald J, Nelissen S, Wei H, Whelan A, Power A, Ahmad A, Miarka M, Gregson M, Maxwell M, Raji R, Lenihan J, Finn-Moloney E, Rafferty M, Cary M, Barale-Thomas E, O'Shea D. Evaluation of the Use of Single- and Multi-Magnification Convolutional Neural Networks for the Determination and Quantitation of Lesions in Nonclinical Pathology Studies. Toxicol Pathol 2021;49:815-42. [PMID: 33618634 DOI: 10.1177/0192623320986423] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
24 Mertes S, Huber T, Weitz K, Heimerl A, André E. GANterfactual—Counterfactual Explanations for Medical Non-experts Using Generative Adversarial Learning. Front Artif Intell 2022;5:825565. [DOI: 10.3389/frai.2022.825565] [Reference Citation Analysis]
25 Tran KA, Kondrashova O, Bradley A, Williams ED, Pearson JV, Waddell N. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med 2021;13:152. [PMID: 34579788 DOI: 10.1186/s13073-021-00968-x] [Reference Citation Analysis]
26 Stenzinger A, Alber M, Allgäuer M, Jurmeister P, Bockmayr M, Budczies J, Lennerz J, Eschrich J, Kazdal D, Schirmacher P, Wagner AH, Tacke F, Capper D, Müller KR, Klauschen F. Artificial intelligence and pathology: From principles to practice and future applications in histomorphology and molecular profiling. Semin Cancer Biol 2021:S1044-579X(21)00034-1. [PMID: 33631297 DOI: 10.1016/j.semcancer.2021.02.011] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
27 Bruckert S, Finzel B, Schmid U. The Next Generation of Medical Decision Support: A Roadmap Toward Transparent Expert Companions. Front Artif Intell 2020;3:507973. [PMID: 33733193 DOI: 10.3389/frai.2020.507973] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
28 Maloca PM, Müller PL, Lee AY, Tufail A, Balaskas K, Niklaus S, Kaiser P, Suter S, Zarranz-Ventura J, Egan C, Scholl HPN, Schnitzer TK, Singer T, Hasler PW, Denk N. Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence. Commun Biol 2021;4:170. [PMID: 33547415 DOI: 10.1038/s42003-021-01697-y] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]