Paper Info
Title
Iterative Attention Mining for Weakly Supervised Thoracic Disease Pattern Localization in Chest X-Rays.
Abstract
Given image labels as the only supervisory signal, we focus on harvesting/mining, thoracic disease localizations from chest X-ray images. Harvesting such localizations from existing datasets allows for the creation of improved data sources for computer-aided diagnosis and retrospective analyses. We train a convolutional neural network (CNN) for image classification and propose an attention mining (AM) strategy to improve the model's sensitivity or saliency to disease patterns. The intuition of AM is that once the most salient disease area is blocked or hidden from the CNN model, it will pay attention to alternative image regions, while still attempting to make correct predictions. However, the model requires to be properly constrained during AM, otherwise, it may overfit to uncorrelated image parts and forget the valuable knowledge that it has learned from the original image classification task. To alleviate such side effects, we then design a knowledge preservation (KP) loss, which minimizes the discrepancy between responses for X-ray images from the original and the updated networks. Furthermore, we modify the CNN model to include multi-scale aggregation (MSA), improving its localization ability on small-scale disease findings, e.g., lung nodules. We validate our method on the publicly-available ChestX-ray14 dataset, outperforming a class activation map (CAM)-based approach, and demonstrating the value of our novel framework for mining disease locations.
Year
DOI
Venue
2018
10.1007/978-3-030-00934-2_66
Lecture Notes in Computer Science
DocType
Volume
ISSN
Conference
11071
0302-9743
Citations 
PageRank 
References 
2
0.39
9
Authors
6
Name
Order
Citations
PageRank
Jinzheng Cai17110.95
Le Lu2129786.78
Adam P. Harrison310117.06
Xiaoshuang Shi411714.35
Pingjun Chen5134.07
Lin Yang61291116.88