150 related articles for article (PubMed ID: 36310480)
1. [Deep learning-based fully automated intelligent and precise diagnosis for melanocytic lesions].
Shi T; Zhang J; Bao Y; Gao X
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2022 Oct; 39(5):919-927. PubMed ID: 36310480
[TBL] [Abstract][Full Text] [Related]
2. Deep learning-based fully automated diagnosis of melanocytic lesions by using whole slide images.
Bao Y; Zhang J; Zhao X; Zhou H; Chen Y; Jian J; Shi T; Gao X
J Dermatolog Treat; 2022 Aug; 33(5):2571-2577. PubMed ID: 35112978
[TBL] [Abstract][Full Text] [Related]
3. Accuracy of Digital Pathologic Analysis vs Traditional Microscopy in the Interpretation of Melanocytic Lesions.
Onega T; Barnhill RL; Piepkorn MW; Longton GM; Elder DE; Weinstock MA; Knezevich SR; Reisch LM; Carney PA; Nelson HD; Radick AC; Elmore JG
JAMA Dermatol; 2018 Oct; 154(10):1159-1166. PubMed ID: 30140929
[TBL] [Abstract][Full Text] [Related]
4. Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts.
Haggenmüller S; Maron RC; Hekler A; Utikal JS; Barata C; Barnhill RL; Beltraminelli H; Berking C; Betz-Stablein B; Blum A; Braun SA; Carr R; Combalia M; Fernandez-Figueras MT; Ferrara G; Fraitag S; French LE; Gellrich FF; Ghoreschi K; Goebeler M; Guitera P; Haenssle HA; Haferkamp S; Heinzerling L; Heppt MV; Hilke FJ; Hobelsberger S; Krahl D; Kutzner H; Lallas A; Liopyris K; Llamas-Velasco M; Malvehy J; Meier F; Müller CSL; Navarini AA; Navarrete-Dechent C; Perasole A; Poch G; Podlipnik S; Requena L; Rotemberg VM; Saggini A; Sangueza OP; Santonja C; Schadendorf D; Schilling B; Schlaak M; Schlager JG; Sergon M; Sondermann W; Soyer HP; Starz H; Stolz W; Vale E; Weyers W; Zink A; Krieghoff-Henning E; Kather JN; von Kalle C; Lipka DB; Fröhling S; Hauschild A; Kittler H; Brinker TJ
Eur J Cancer; 2021 Oct; 156():202-216. PubMed ID: 34509059
[TBL] [Abstract][Full Text] [Related]
5. A state-of-the-art survey of artificial neural networks for Whole-slide Image analysis: From popular Convolutional Neural Networks to potential visual transformers.
Hu W; Li X; Li C; Li R; Jiang T; Sun H; Huang X; Grzegorzek M; Li X
Comput Biol Med; 2023 Jul; 161():107034. PubMed ID: 37230019
[TBL] [Abstract][Full Text] [Related]
6. An attention-based weakly supervised framework for spitzoid melanocytic lesion diagnosis in whole slide images.
Del Amor R; Launet L; Colomer A; Moscardó A; Mosquera-Zamudio A; Monteagudo C; Naranjo V
Artif Intell Med; 2021 Nov; 121():102197. PubMed ID: 34763799
[TBL] [Abstract][Full Text] [Related]
7. Deep Learning Approaches Towards Skin Lesion Segmentation and Classification from Dermoscopic Images - A Review.
Baig R; Bibi M; Hamid A; Kausar S; Khalid S
Curr Med Imaging; 2020; 16(5):513-533. PubMed ID: 32484086
[TBL] [Abstract][Full Text] [Related]
8. Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification.
Al-Masni MA; Kim DH; Kim TS
Comput Methods Programs Biomed; 2020 Jul; 190():105351. PubMed ID: 32028084
[TBL] [Abstract][Full Text] [Related]
9. Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis.
Wang X; Chen H; Gan C; Lin H; Dou Q; Tsougenis E; Huang Q; Cai M; Heng PA
IEEE Trans Cybern; 2020 Sep; 50(9):3950-3962. PubMed ID: 31484154
[TBL] [Abstract][Full Text] [Related]
10. Automated identification of malignancy in whole-slide pathological images: identification of eyelid malignant melanoma in gigapixel pathological slides using deep learning.
Wang L; Ding L; Liu Z; Sun L; Chen L; Jia R; Dai X; Cao J; Ye J
Br J Ophthalmol; 2020 Mar; 104(3):318-323. PubMed ID: 31302629
[TBL] [Abstract][Full Text] [Related]
11. A weakly supervised deep learning-based method for glioma subtype classification using WSI and mpMRIs.
Hsu WW; Guo JM; Pei L; Chiang LA; Li YF; Hsiao JC; Colen R; Liu P
Sci Rep; 2022 Apr; 12(1):6111. PubMed ID: 35414643
[TBL] [Abstract][Full Text] [Related]
12. Deep Learning Approach to Classify Cutaneous Melanoma in a Whole Slide Image.
Li M; Abe M; Nakano S; Tsuneki M
Cancers (Basel); 2023 Mar; 15(6):. PubMed ID: 36980793
[TBL] [Abstract][Full Text] [Related]
13. Deep learning-based framework for slide-based histopathological image analysis.
Kosaraju S; Park J; Lee H; Yang JW; Kang M
Sci Rep; 2022 Nov; 12(1):19075. PubMed ID: 36351997
[TBL] [Abstract][Full Text] [Related]
14. Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma.
Miyoshi H; Sato K; Kabeya Y; Yonezawa S; Nakano H; Takeuchi Y; Ozawa I; Higo S; Yanagida E; Yamada K; Kohno K; Furuta T; Muta H; Takeuchi M; Sasaki Y; Yoshimura T; Matsuda K; Muto R; Moritsubo M; Inoue K; Suzuki T; Sekinaga H; Ohshima K
Lab Invest; 2020 Oct; 100(10):1300-1310. PubMed ID: 32472096
[TBL] [Abstract][Full Text] [Related]
15. Automated Diagnosis and Localization of Melanoma from Skin Histopathology Slides Using Deep Learning: A Multicenter Study.
Li T; Xie P; Liu J; Chen M; Zhao S; Kang W; Zuo K; Li F
J Healthc Eng; 2021; 2021():5972962. PubMed ID: 34745503
[TBL] [Abstract][Full Text] [Related]
16. Generating region proposals for histopathological whole slide image retrieval.
Ma Y; Jiang Z; Zhang H; Xie F; Zheng Y; Shi H; Zhao Y; Shi J
Comput Methods Programs Biomed; 2018 Jun; 159():1-10. PubMed ID: 29650303
[TBL] [Abstract][Full Text] [Related]
17. SCDNet: A Deep Learning-Based Framework for the Multiclassification of Skin Cancer Using Dermoscopy Images.
Naeem A; Anees T; Fiza M; Naqvi RA; Lee SW
Sensors (Basel); 2022 Jul; 22(15):. PubMed ID: 35957209
[TBL] [Abstract][Full Text] [Related]
18. Recent developments in cervical cancer diagnosis using deep learning on whole slide images: An Overview of models, techniques, challenges and future directions.
Sambyal D; Sarwar A
Micron; 2023 Oct; 173():103520. PubMed ID: 37556898
[TBL] [Abstract][Full Text] [Related]
19. Computer-Aided Assessment of Melanocytic Lesions by Means of a Mitosis Algorithm.
Sturm B; Creytens D; Smits J; Ooms AHAG; Eijken E; Kurpershoek E; Küsters-Vandevelde HVN; Wauters C; Blokx WAM; van der Laak JAWM
Diagnostics (Basel); 2022 Feb; 12(2):. PubMed ID: 35204526
[TBL] [Abstract][Full Text] [Related]
20. Adaptive color deconvolution for histological WSI normalization.
Zheng Y; Jiang Z; Zhang H; Xie F; Shi J; Xue C
Comput Methods Programs Biomed; 2019 Mar; 170():107-120. PubMed ID: 30712599
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
