872 related articles for article (PubMed ID: 33387492)
21. Deep learning detects genetic alterations in cancer histology generated by adversarial networks.
Krause J; Grabsch HI; Kloor M; Jendrusch M; Echle A; Buelow RD; Boor P; Luedde T; Brinker TJ; Trautwein C; Pearson AT; Quirke P; Jenniskens J; Offermans K; van den Brandt PA; Kather JN
J Pathol; 2021 May; 254(1):70-79. PubMed ID: 33565124
[TBL] [Abstract][Full Text] [Related]
22. Artificial intelligence for detection of microsatellite instability in colorectal cancer-a multicentric analysis of a pre-screening tool for clinical application.
Echle A; Ghaffari Laleh N; Quirke P; Grabsch HI; Muti HS; Saldanha OL; Brockmoeller SF; van den Brandt PA; Hutchins GGA; Richman SD; Horisberger K; Galata C; Ebert MP; Eckardt M; Boutros M; Horst D; Reissfelder C; Alwers E; Brinker TJ; Langer R; Jenniskens JCA; Offermans K; Mueller W; Gray R; Gruber SB; Greenson JK; Rennert G; Bonner JD; Schmolze D; Chang-Claude J; Brenner H; Trautwein C; Boor P; Jaeger D; Gaisa NT; Hoffmeister M; West NP; Kather JN
ESMO Open; 2022 Apr; 7(2):100400. PubMed ID: 35247870
[TBL] [Abstract][Full Text] [Related]
23. A U-Net based framework to quantify glomerulosclerosis in digitized PAS and H&E stained human tissues.
Gallego J; Swiderska-Chadaj Z; Markiewicz T; Yamashita M; Gabaldon MA; Gertych A
Comput Med Imaging Graph; 2021 Apr; 89():101865. PubMed ID: 33548823
[TBL] [Abstract][Full Text] [Related]
24. Deep learning image analysis quantifies tumor heterogeneity and identifies microsatellite instability in colon cancer.
Rubinstein JC; Foroughi Pour A; Zhou J; Sheridan TB; White BS; Chuang JH
J Surg Oncol; 2023 Mar; 127(3):426-433. PubMed ID: 36251352
[TBL] [Abstract][Full Text] [Related]
25. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study.
Bulten W; Pinckaers H; van Boven H; Vink R; de Bel T; van Ginneken B; van der Laak J; Hulsbergen-van de Kaa C; Litjens G
Lancet Oncol; 2020 Feb; 21(2):233-241. PubMed ID: 31926805
[TBL] [Abstract][Full Text] [Related]
26. Deep learning-based prediction of treatment prognosis from nasal polyp histology slides.
Wang K; Ren Y; Ma L; Fan Y; Yang Z; Yang Q; Shi J; Sun Y
Int Forum Allergy Rhinol; 2023 May; 13(5):886-898. PubMed ID: 36066094
[TBL] [Abstract][Full Text] [Related]
27. DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer.
Schirris Y; Gavves E; Nederlof I; Horlings HM; Teuwen J
Med Image Anal; 2022 Jul; 79():102464. PubMed ID: 35596966
[TBL] [Abstract][Full Text] [Related]
28. Use of Deep Learning to Develop and Analyze Computational Hematoxylin and Eosin Staining of Prostate Core Biopsy Images for Tumor Diagnosis.
Rana A; Lowe A; Lithgow M; Horback K; Janovitz T; Da Silva A; Tsai H; Shanmugam V; Bayat A; Shah P
JAMA Netw Open; 2020 May; 3(5):e205111. PubMed ID: 32432709
[TBL] [Abstract][Full Text] [Related]
29. MuDeRN: Multi-category classification of breast histopathological image using deep residual networks.
Gandomkar Z; Brennan PC; Mello-Thoms C
Artif Intell Med; 2018 Jun; 88():14-24. PubMed ID: 29705552
[TBL] [Abstract][Full Text] [Related]
30. Piloting a Deep Learning Model for Predicting Nuclear BAP1 Immunohistochemical Expression of Uveal Melanoma from Hematoxylin-and-Eosin Sections.
Zhang H; Kalirai H; Acha-Sagredo A; Yang X; Zheng Y; Coupland SE
Transl Vis Sci Technol; 2020 Sep; 9(2):50. PubMed ID: 32953248
[TBL] [Abstract][Full Text] [Related]
31. Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology.
Schrammen PL; Ghaffari Laleh N; Echle A; Truhn D; Schulz V; Brinker TJ; Brenner H; Chang-Claude J; Alwers E; Brobeil A; Kloor M; Heij LR; Jäger D; Trautwein C; Grabsch HI; Quirke P; West NP; Hoffmeister M; Kather JN
J Pathol; 2022 Jan; 256(1):50-60. PubMed ID: 34561876
[TBL] [Abstract][Full Text] [Related]
32. Automated diagnosis of 7 canine skin tumors using machine learning on H&E-stained whole slide images.
Fragoso-Garcia M; Wilm F; Bertram CA; Merz S; Schmidt A; Donovan T; Fuchs-Baumgartinger A; Bartel A; Marzahl C; Diehl L; Puget C; Maier A; Aubreville M; Breininger K; Klopfleisch R
Vet Pathol; 2023 Nov; 60(6):865-875. PubMed ID: 37515411
[TBL] [Abstract][Full Text] [Related]
33. Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts.
Fremond S; Andani S; Barkey Wolf J; Dijkstra J; Melsbach S; Jobsen JJ; Brinkhuis M; Roothaan S; Jurgenliemk-Schulz I; Lutgens LCHW; Nout RA; van der Steen-Banasik EM; de Boer SM; Powell ME; Singh N; Mileshkin LR; Mackay HJ; Leary A; Nijman HW; Smit VTHBM; Creutzberg CL; Horeweg N; Koelzer VH; Bosse T
Lancet Digit Health; 2023 Feb; 5(2):e71-e82. PubMed ID: 36496303
[TBL] [Abstract][Full Text] [Related]
34. Validation of MSIntuit as an AI-based pre-screening tool for MSI detection from colorectal cancer histology slides.
Saillard C; Dubois R; Tchita O; Loiseau N; Garcia T; Adriansen A; Carpentier S; Reyre J; Enea D; von Loga K; Kamoun A; Rossat S; Wiscart C; Sefta M; Auffret M; Guillou L; Fouillet A; Kather JN; Svrcek M
Nat Commun; 2023 Nov; 14(1):6695. PubMed ID: 37932267
[TBL] [Abstract][Full Text] [Related]
35. Automated curation of large-scale cancer histopathology image datasets using deep learning.
Hilgers L; Ghaffari Laleh N; West NP; Westwood A; Hewitt KJ; Quirke P; Grabsch HI; Carrero ZI; Matthaei E; Loeffler CML; Brinker TJ; Yuan T; Brenner H; Brobeil A; Hoffmeister M; Kather JN
Histopathology; 2024 Jun; 84(7):1139-1153. PubMed ID: 38409878
[TBL] [Abstract][Full Text] [Related]
36. Computed Tomography-Based Radiomic Features Could Potentially Predict Microsatellite Instability Status in Stage II Colorectal Cancer: A Preliminary Study.
Fan S; Li X; Cui X; Zheng L; Ren X; Ma W; Ye Z
Acad Radiol; 2019 Dec; 26(12):1633-1640. PubMed ID: 30929999
[TBL] [Abstract][Full Text] [Related]
37. Predictive model for high-frequency microsatellite instability in colorectal cancer patients over 50 years of age.
Fujiyoshi K; Yamaguchi T; Kakuta M; Takahashi A; Arai Y; Yamada M; Yamamoto G; Ohde S; Takao M; Horiguchi SI; Natsume S; Kazama S; Nishizawa Y; Nishimura Y; Akagi Y; Sakamoto H; Akagi K
Cancer Med; 2017 Jun; 6(6):1255-1263. PubMed ID: 28544821
[TBL] [Abstract][Full Text] [Related]
38. A National Cancer Institute Workshop on Microsatellite Instability for cancer detection and familial predisposition: development of international criteria for the determination of microsatellite instability in colorectal cancer.
Boland CR; Thibodeau SN; Hamilton SR; Sidransky D; Eshleman JR; Burt RW; Meltzer SJ; Rodriguez-Bigas MA; Fodde R; Ranzani GN; Srivastava S
Cancer Res; 1998 Nov; 58(22):5248-57. PubMed ID: 9823339
[TBL] [Abstract][Full Text] [Related]
39. Evaluating the Microsatellite Instability of Colorectal Cancer Based on Multimodal Deep Learning Integrating Histopathological and Molecular Data.
Qiu W; Yang J; Wang B; Yang M; Tian G; Wang P; Yang J
Front Oncol; 2022; 12():925079. PubMed ID: 35865460
[TBL] [Abstract][Full Text] [Related]
40. Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study.
Muti HS; Heij LR; Keller G; Kohlruss M; Langer R; Dislich B; Cheong JH; Kim YW; Kim H; Kook MC; Cunningham D; Allum WH; Langley RE; Nankivell MG; Quirke P; Hayden JD; West NP; Irvine AJ; Yoshikawa T; Oshima T; Huss R; Grosser B; Roviello F; d'Ignazio A; Quaas A; Alakus H; Tan X; Pearson AT; Luedde T; Ebert MP; Jäger D; Trautwein C; Gaisa NT; Grabsch HI; Kather JN
Lancet Digit Health; 2021 Oct; 3(10):e654-e664. PubMed ID: 34417147
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
