Deep Learning for Whole-Slide Tissue Histopathology Classification: A Comparative Study in the Identification of Dysplastic and Non-Dysplastic Barrett's Esophagus

被引:17
作者
Sali, Rasoul [1 ]
Moradinasab, Nazanin [1 ]
Guleria, Shan [2 ]
Ehsan, Lubaina [3 ]
Fernandes, Philip [3 ]
Shah, Tilak U. [4 ,5 ]
Syed, Sana [3 ]
Brown, Donald E. [1 ,6 ]
机构
[1] Univ Virginia, Dept Syst & Informat Engn, Charlottesville, VA 22904 USA
[2] Rush Univ, Dept Internal Med, Med Ctr, Chicago, IL 60612 USA
[3] Univ Virginia, Sch Med, Charlottesville, VA 22903 USA
[4] Hunter Holmes McGuire Vet Affairs Med Ctr, Richmond, VA 23249 USA
[5] Virginia Commonwealth Univ, Div Gastroenterol Hepatol & Nutr, Richmond, VA 23219 USA
[6] Univ Virginia, Sch Data Sci, Charlottesville, VA 22904 USA
基金
美国国家卫生研究院;
关键词
deep learning; whole-slide tissue histopathology; feature extraction approaches; Barrett's esophagus; LOW-GRADE DYSPLASIA; DIAGNOSIS; MANAGEMENT; FEATURES; CANCER;
D O I
10.3390/jpm10040141
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
The gold standard of histopathology for the diagnosis of Barrett's esophagus (BE) is hindered by inter-observer variability among gastrointestinal pathologists. Deep learning-based approaches have shown promising results in the analysis of whole-slide tissue histopathology images (WSIs). We performed a comparative study to elucidate the characteristics and behaviors of different deep learning-based feature representation approaches for the WSI-based diagnosis of diseased esophageal architectures, namely, dysplastic and non-dysplastic BE. The results showed that if appropriate settings are chosen, the unsupervised feature representation approach is capable of extracting more relevant image features from WSIs to classify and locate the precursors of esophageal cancer compared to weakly supervised and fully supervised approaches.
引用
收藏
页码:1 / 16
页数:16
相关论文
共 37 条
[1]   Representation learning-based unsupervised domain adaptation for classification of breast cancer histopathology images [J].
Alirezazadeh, Pendar ;
Hejrati, Behzad ;
Monsef-Esfahani, Alireza ;
Fathi, Abdolhossein .
BIOCYBERNETICS AND BIOMEDICAL ENGINEERING, 2018, 38 (03) :671-683
[2]   Multiple instance classification: Review, taxonomy and comparative study [J].
Amores, Jaume .
ARTIFICIAL INTELLIGENCE, 2013, 201 :81-105
[3]  
Bishop C.M., 2006, Pattern recognition and machine learning
[4]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[5]  
Caicedo JC, 2009, LECT NOTES ARTIF INT, V5651, P126, DOI 10.1007/978-3-642-02976-9_17
[6]   Clinical-grade computational pathology using weakly supervised deep learning on whole slide images [J].
Campanella, Gabriele ;
Hanna, Matthew G. ;
Geneslaw, Luke ;
Miraflor, Allen ;
Silva, Vitor Werneck Krauss ;
Busam, Klaus J. ;
Brogi, Edi ;
Reuter, Victor E. ;
Klimstra, David S. ;
Fuchs, Thomas J. .
NATURE MEDICINE, 2019, 25 (08) :1301-+
[7]   Stacked Predictive Sparse Decomposition for Classification of Histology Sections [J].
Chang, Hang ;
Zhou, Yin ;
Borowsky, Alexander ;
Barner, Kenneth ;
Spellman, Paul ;
Parvin, Bahram .
INTERNATIONAL JOURNAL OF COMPUTER VISION, 2015, 113 (01) :3-18
[8]   High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection [J].
Cruz-Roa, Angel ;
Gilmore, Hannah ;
Basavanhally, Ajay ;
Feldman, Michael ;
Ganesan, Shridar ;
Shih, Natalie ;
Tomaszewski, John ;
Madabhushi, Anant ;
Gonzalez, Fabio .
PLOS ONE, 2018, 13 (05)
[9]   Automatic detection of invasive ductal carcinoma in whole slide images with Convolutional Neural Networks [J].
Cruz-Roa, Angel ;
Basavanhally, Ajay ;
Gonzalez, Fabio ;
Gilmore, Hannah ;
Feldman, Michael ;
Ganesan, Shridar ;
Shih, Natalie ;
Tomaszewski, John ;
Madabhushi, Anant .
MEDICAL IMAGING 2014: DIGITAL PATHOLOGY, 2014, 9041
[10]   Poor Interobserver Agreement in the Distinction of High-Grade Dysplasia and Adenocarcinoma in Pretreatment Barrett's Esophagus Biopsies [J].
Downs-Kelly, Erinn ;
Mendelin, Joel E. ;
Bennett, Ana E. ;
Castilla, Elias ;
Henricks, Walter H. ;
Schoenfield, Lynn ;
Skacel, Marek ;
Yerian, Lisa ;
Rice, Thomas W. ;
Rybicki, Lisa A. ;
Bronner, Mary P. ;
Goldblum, John R. .
AMERICAN JOURNAL OF GASTROENTEROLOGY, 2008, 103 (09) :2333-2340