John Arevalo1, Angel Cruz-Roa2, Viviana Arias3, Eduardo Romero4, Fabio A González5. 1. Machine Learning, Perception and Discovery Lab, Systems and Computer Engineering Department, Universidad Nacional de Colombia, Faculty of Engineering, Cra 30 No 45 03-Ciudad Universitaria, Building 453 Office 114, Bogotá DC, Colombia. Electronic address: jearevaloo@unal.edu.co. 2. Machine Learning, Perception and Discovery Lab, Systems and Computer Engineering Department, Universidad Nacional de Colombia, Faculty of Engineering, Cra 30 No 45 03-Ciudad Universitaria, Building 453 Office 114, Bogotá DC, Colombia. Electronic address: aacruzr@unal.edu.co. 3. Pathology Department, Universidad Nacional de Colombia, Faculty of Medicine, Cra 30 No 45 03-Ciudad Universitaria, Bogotá DC, Colombia. Electronic address: vlariasp@unal.edu.co. 4. Computer Imaging & Medical Applications Laboratory, Universidad Nacional de Colombia, Faculty of Medicine, Cra 30 No 45 03-Ciudad Universitaria, Bogotá DC, Colombia. Electronic address: edromero@unal.edu.co. 5. Machine Learning, Perception and Discovery Lab, Systems and Computer Engineering Department, Universidad Nacional de Colombia, Faculty of Engineering, Cra 30 No 45 03-Ciudad Universitaria, Building 453 Office 114, Bogotá DC, Colombia. Electronic address: fagonzalezo@unal.edu.co.
Abstract
OBJECTIVE: The paper addresses the problem of automatic detection of basal cell carcinoma (BCC) in histopathology images. In particular, it proposes a framework to both, learn the image representation in an unsupervised way and visualize discriminative features supported by the learned model. MATERIALS AND METHODS: This paper presents an integrated unsupervised feature learning (UFL) framework for histopathology image analysis that comprises three main stages: (1) local (patch) representation learning using different strategies (sparse autoencoders, reconstruct independent component analysis and topographic independent component analysis (TICA), (2) global (image) representation learning using a bag-of-features representation or a convolutional neural network, and (3) a visual interpretation layer to highlight the most discriminant regions detected by the model. The integrated unsupervised feature learning framework was exhaustively evaluated in a histopathology image dataset for BCC diagnosis. RESULTS: The experimental evaluation produced a classification performance of 98.1%, in terms of the area under receiver-operating-characteristic curve, for the proposed framework outperforming by 7% the state-of-the-art discrete cosine transform patch-based representation. CONCLUSIONS: The proposed UFL-representation-based approach outperforms state-of-the-art methods for BCC detection. Thanks to its visual interpretation layer, the method is able to highlight discriminative tissue regions providing a better diagnosis support. Among the different UFL strategies tested, TICA-learned features exhibited the best performance thanks to its ability to capture low-level invariances, which are inherent to the nature of the problem.
OBJECTIVE: The paper addresses the problem of automatic detection of basal cell carcinoma (BCC) in histopathology images. In particular, it proposes a framework to both, learn the image representation in an unsupervised way and visualize discriminative features supported by the learned model. MATERIALS AND METHODS: This paper presents an integrated unsupervised feature learning (UFL) framework for histopathology image analysis that comprises three main stages: (1) local (patch) representation learning using different strategies (sparse autoencoders, reconstruct independent component analysis and topographic independent component analysis (TICA), (2) global (image) representation learning using a bag-of-features representation or a convolutional neural network, and (3) a visual interpretation layer to highlight the most discriminant regions detected by the model. The integrated unsupervised feature learning framework was exhaustively evaluated in a histopathology image dataset for BCC diagnosis. RESULTS: The experimental evaluation produced a classification performance of 98.1%, in terms of the area under receiver-operating-characteristic curve, for the proposed framework outperforming by 7% the state-of-the-art discrete cosine transform patch-based representation. CONCLUSIONS: The proposed UFL-representation-based approach outperforms state-of-the-art methods for BCC detection. Thanks to its visual interpretation layer, the method is able to highlight discriminative tissue regions providing a better diagnosis support. Among the different UFL strategies tested, TICA-learned features exhibited the best performance thanks to its ability to capture low-level invariances, which are inherent to the nature of the problem.
Authors: Eliot G Peyster; Sara Arabyarmohammadi; Andrew Janowczyk; Sepideh Azarianpour-Esfahani; Miroslav Sekulic; Clarissa Cassol; Luke Blower; Anil Parwani; Priti Lal; Michael D Feldman; Kenneth B Margulies; Anant Madabhushi Journal: Eur Heart J Date: 2021-06-21 Impact factor: 35.855
Authors: Sudhir Sornapudi; Ronald Joe Stanley; William V Stoecker; Haidar Almubarak; Rodney Long; Sameer Antani; George Thoma; Rosemary Zuna; Shelliane R Frazier Journal: J Pathol Inform Date: 2018-03-05
Authors: Sudhir Sornapudi; Jason Hagerty; R Joe Stanley; William V Stoecker; Rodney Long; Sameer Antani; George Thoma; Rosemary Zuna; Shellaine R Frazier Journal: J Pathol Inform Date: 2020-03-30