716 related articles for article (PubMed ID: 36057226)
1. PPsNet: An improved deep learning model for microsatellite instability high prediction in colorectal cancer from whole slide images.
Lou J; Xu J; Zhang Y; Sun Y; Fang A; Liu J; Mur LAJ; Ji B
Comput Methods Programs Biomed; 2022 Oct; 225():107095. PubMed ID: 36057226
[TBL] [Abstract][Full Text] [Related]
2. Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study.
Yamashita R; Long J; Longacre T; Peng L; Berry G; Martin B; Higgins J; Rubin DL; Shen J
Lancet Oncol; 2021 Jan; 22(1):132-141. PubMed ID: 33387492
[TBL] [Abstract][Full Text] [Related]
3. Feasibility of deep learning-based fully automated classification of microsatellite instability in tissue slides of colorectal cancer.
Lee SH; Song IH; Jang HJ
Int J Cancer; 2021 Aug; 149(3):728-740. PubMed ID: 33851412
[TBL] [Abstract][Full Text] [Related]
4. Deep learning-based methods for classification of microsatellite instability in endometrial cancer from HE-stained pathological images.
Zhang Y; Chen S; Wang Y; Li J; Xu K; Chen J; Zhao J
J Cancer Res Clin Oncol; 2023 Sep; 149(11):8877-8888. PubMed ID: 37150803
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Detecting microsatellite instability in colorectal cancer using Transformer-based colonoscopy image classification and retrieval.
Lo CM; Jiang JK; Lin CC
PLoS One; 2024; 19(1):e0292277. PubMed ID: 38271352
[TBL] [Abstract][Full Text] [Related]
7. Detecting immunotherapy-sensitive subtype in gastric cancer using histologic image-based deep learning.
Hinata M; Ushiku T
Sci Rep; 2021 Nov; 11(1):22636. PubMed ID: 34811485
[TBL] [Abstract][Full Text] [Related]
8. Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study.
Bilal M; Raza SEA; Azam A; Graham S; Ilyas M; Cree IA; Snead D; Minhas F; Rajpoot NM
Lancet Digit Health; 2021 Dec; 3(12):e763-e772. PubMed ID: 34686474
[TBL] [Abstract][Full Text] [Related]
9. Development and validation of a radiomics-based nomogram for the preoperative prediction of microsatellite instability in colorectal cancer.
Ying M; Pan J; Lu G; Zhou S; Fu J; Wang Q; Wang L; Hu B; Wei Y; Shen J
BMC Cancer; 2022 May; 22(1):524. PubMed ID: 35534797
[TBL] [Abstract][Full Text] [Related]
10. Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer.
Cao R; Yang F; Ma SC; Liu L; Zhao Y; Li Y; Wu DH; Wang T; Lu WJ; Cai WJ; Zhu HB; Guo XJ; Lu YW; Kuang JJ; Huan WJ; Tang WM; Huang K; Huang J; Yao J; Dong ZY
Theranostics; 2020; 10(24):11080-11091. PubMed ID: 33042271
[TBL] [Abstract][Full Text] [Related]
11. Attention-based multiple instance learning with self-supervision to predict microsatellite instability in colorectal cancer from histology whole-slide images.
Leiby JS; Hao J; Kang GH; Park JW; Kim D
Annu Int Conf IEEE Eng Med Biol Soc; 2022 Jul; 2022():3068-3071. PubMed ID: 36085965
[TBL] [Abstract][Full Text] [Related]
12. Predicting microsatellite instability and key biomarkers in colorectal cancer from H&E-stained images: achieving state-of-the-art predictive performance with fewer data using Swin Transformer.
Guo B; Li X; Yang M; Jonnagaddala J; Zhang H; Xu XS
J Pathol Clin Res; 2023 May; 9(3):223-235. PubMed ID: 36723384
[TBL] [Abstract][Full Text] [Related]
13. Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning.
Jang HJ; Lee A; Kang J; Song IH; Lee SH
World J Gastroenterol; 2020 Oct; 26(40):6207-6223. PubMed ID: 33177794
[TBL] [Abstract][Full Text] [Related]
14. Clinical-Grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning.
Echle A; Grabsch HI; Quirke P; van den Brandt PA; West NP; Hutchins GGA; Heij LR; Tan X; Richman SD; Krause J; Alwers E; Jenniskens J; Offermans K; Gray R; Brenner H; Chang-Claude J; Trautwein C; Pearson AT; Boor P; Luedde T; Gaisa NT; Hoffmeister M; Kather JN
Gastroenterology; 2020 Oct; 159(4):1406-1416.e11. PubMed ID: 32562722
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Development and validation of MRI-based deep learning models for prediction of microsatellite instability in rectal cancer.
Zhang W; Yin H; Huang Z; Zhao J; Zheng H; He D; Li M; Tan W; Tian S; Song B
Cancer Med; 2021 Jun; 10(12):4164-4173. PubMed ID: 33963688
[TBL] [Abstract][Full Text] [Related]
17. Non-invasive prediction of microsatellite instability in colorectal cancer by a genetic algorithm-enhanced artificial neural network-based CT radiomics signature.
Chen X; He L; Li Q; Liu L; Li S; Zhang Y; Liu Z; Huang Y; Mao Y; Chen X
Eur Radiol; 2023 Jan; 33(1):11-22. PubMed ID: 35771245
[TBL] [Abstract][Full Text] [Related]
18. Predicting colorectal cancer microsatellite instability with a self-attention-enabled convolutional neural network.
Chang X; Wang J; Zhang G; Yang M; Xi Y; Xi C; Chen G; Nie X; Meng B; Quan X
Cell Rep Med; 2023 Feb; 4(2):100914. PubMed ID: 36720223
[TBL] [Abstract][Full Text] [Related]
19. Development and validation of a deep learning-based microsatellite instability predictor from prostate cancer whole-slide images.
Hu Q; Rizvi AA; Schau G; Ingale K; Muller Y; Baits R; Pretzer S; BenTaieb A; Gordhamer A; Nussenzveig R; Cole A; Leavitt MO; Jones RD; Joshi RP; Beaubier N; Stumpe MC; Nagpal K
NPJ Precis Oncol; 2024 Apr; 8(1):88. PubMed ID: 38594360
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
