133 related articles for article (PubMed ID: 36268061)
1. A deep learning framework for automated classification of histopathological kidney whole-slide images.
Abdeltawab HA; Khalifa FA; Ghazal MA; Cheng L; El-Baz AS; Gondim DD
J Pathol Inform; 2022; 13():100093. PubMed ID: 36268061
[TBL] [Abstract][Full Text] [Related]
2. A pyramidal deep learning pipeline for kidney whole-slide histology images classification.
Abdeltawab H; Khalifa F; Ghazal M; Cheng L; Gondim D; El-Baz A
Sci Rep; 2021 Oct; 11(1):20189. PubMed ID: 34642404
[TBL] [Abstract][Full Text] [Related]
3. Development and Evaluation of a Novel Deep-Learning-Based Framework for the Classification of Renal Histopathology Images.
Abu Haeyeh Y; Ghazal M; El-Baz A; Talaat IM
Bioengineering (Basel); 2022 Aug; 9(9):. PubMed ID: 36134972
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Deep learning and radiomics: the utility of Google TensorFlowâ„¢ Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT.
Coy H; Hsieh K; Wu W; Nagarajan MB; Young JR; Douek ML; Brown MS; Scalzo F; Raman SS
Abdom Radiol (NY); 2019 Jun; 44(6):2009-2020. PubMed ID: 30778739
[TBL] [Abstract][Full Text] [Related]
6. Pan-Renal Cell Carcinoma classification and survival prediction from histopathology images using deep learning.
Tabibu S; Vinod PK; Jawahar CV
Sci Rep; 2019 Jul; 9(1):10509. PubMed ID: 31324828
[TBL] [Abstract][Full Text] [Related]
7. Development of a Deep Learning Model to Assist With Diagnosis of Hepatocellular Carcinoma.
Feng S; Yu X; Liang W; Li X; Zhong W; Hu W; Zhang H; Feng Z; Song M; Zhang J; Zhang X
Front Oncol; 2021; 11():762733. PubMed ID: 34926264
[TBL] [Abstract][Full Text] [Related]
8. TGMIL: A hybrid multi-instance learning model based on the Transformer and the Graph Attention Network for whole-slide images classification of renal cell carcinoma.
Sun X; Li W; Fu B; Peng Y; He J; Wang L; Yang T; Meng X; Li J; Wang J; Huang P; Wang R
Comput Methods Programs Biomed; 2023 Dec; 242():107789. PubMed ID: 37722310
[TBL] [Abstract][Full Text] [Related]
9. Artificial intelligence-based multi-class histopathologic classification of kidney neoplasms.
Gondim DD; Al-Obaidy KI; Idrees MT; Eble JN; Cheng L
J Pathol Inform; 2023; 14():100299. PubMed ID: 36915914
[TBL] [Abstract][Full Text] [Related]
10. Recognizing basal cell carcinoma on smartphone-captured digital histopathology images with a deep neural network.
Jiang YQ; Xiong JH; Li HY; Yang XH; Yu WT; Gao M; Zhao X; Ma YP; Zhang W; Guan YF; Gu H; Sun JF
Br J Dermatol; 2020 Mar; 182(3):754-762. PubMed ID: 31017653
[TBL] [Abstract][Full Text] [Related]
11. A novel dataset and efficient deep learning framework for automated grading of renal cell carcinoma from kidney histopathology images.
Chanchal AK; Lal S; Kumar R; Kwak JT; Kini J
Sci Rep; 2023 Apr; 13(1):5728. PubMed ID: 37029115
[TBL] [Abstract][Full Text] [Related]
12. Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology.
Sharma H; Zerbe N; Klempert I; Hellwich O; Hufnagl P
Comput Med Imaging Graph; 2017 Nov; 61():2-13. PubMed ID: 28676295
[TBL] [Abstract][Full Text] [Related]
13. Deep-Hipo: Multi-scale receptive field deep learning for histopathological image analysis.
Kosaraju SC; Hao J; Koh HM; Kang M
Methods; 2020 Jul; 179():3-13. PubMed ID: 32442672
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Differentiation of urothelial carcinoma in histopathology images using deep learning and visualization.
Mundhada A; Sundaram S; Swaminathan R; D' Cruze L; Govindarajan S; Makaram N
J Pathol Inform; 2023; 14():100155. PubMed ID: 36523610
[TBL] [Abstract][Full Text] [Related]
16. LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&E stained liver histopathology images.
Aatresh AA; Alabhya K; Lal S; Kini J; Saxena PUP
Int J Comput Assist Radiol Surg; 2021 Sep; 16(9):1549-1563. PubMed ID: 34053009
[TBL] [Abstract][Full Text] [Related]
17. Deep learning-based six-type classifier for lung cancer and mimics from histopathological whole slide images: a retrospective study.
Yang H; Chen L; Cheng Z; Yang M; Wang J; Lin C; Wang Y; Huang L; Chen Y; Peng S; Ke Z; Li W
BMC Med; 2021 Mar; 19(1):80. PubMed ID: 33775248
[TBL] [Abstract][Full Text] [Related]
18. Deep Learning-Based Classification of Epithelial-Mesenchymal Transition for Predicting Response to Therapy in Clear Cell Renal Cell Carcinoma.
Chen Q; Kuai Y; Wang S; Zhu X; Wang H; Liu W; Cheng L; Yang D
Front Oncol; 2021; 11():782515. PubMed ID: 35141144
[TBL] [Abstract][Full Text] [Related]
19. Attention2majority: Weak multiple instance learning for regenerative kidney grading on whole slide images.
Su Z; Tavolara TE; Carreno-Galeano G; Lee SJ; Gurcan MN; Niazi MKK
Med Image Anal; 2022 Jul; 79():102462. PubMed ID: 35512532
[TBL] [Abstract][Full Text] [Related]
20. Automated segmentation of cell membranes to evaluate HER2 status in whole slide images using a modified deep learning network.
Khameneh FD; Razavi S; Kamasak M
Comput Biol Med; 2019 Jul; 110():164-174. PubMed ID: 31163391
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]