Constrained Contrastive Representation: Classification On Chest X-Rays With Limited Data
- Resource Type
- Conference
- Authors
- Zhang, Weiqi; Wang, Hongbo; Lai, Zhiping; Hou, Chao
- Source
- 2021 IEEE International Conference on Multimedia and Expo (ICME) Multimedia and Expo (ICME), 2021 IEEE International Conference on. :1-6 Jul, 2021
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Signal Processing and Analysis
Training
Heating systems
Annotations
Pulmonary diseases
Feature extraction
Lesions
Task analysis
Computer-aided diagnosis
limited data
chest X-ray
semi-supervised learning
contrastive learning
- Language
- ISSN
- 1945-788X
One of the challenges in the field of medical image classification is the expensiveness of labeled data. Most of the previous computer-aided diagnostic methods are based on a paradigm of object detection. Such ways need tons of labeled sample images with positioning annotations, which always need practicing radiologists to process data manually. We focus on Chest X-ray(CXR) images classification and propose an effective framework for lung disease diagnosis based on a self-supervised feature extracting mechanism trained in a constrained contrastive method. Our proposed framework can train on a relatively small dataset in a semi-supervised way and without any positioning annotation. We experiment with the proposed framework on several lung disease diagnosis tasks, including pneumonia and tuberculosis diagnosis, and obtain state-of-the-art results even outperform previous supervised transfer-learning methods.