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CAiD: Context-Aware Instance Discrimination for Self-supervised Learning in Medical Imaging.

Mohammad Reza Hosseinzadeh Taher¹, Fatemeh Haghighi¹, Michael B Gotway²

¹Arizona State University, AZ, USA.

Proceedings of Machine Learning Research

|December 29, 2022

Summary

This summary is machine-generated.

Context-Aware instance Discrimination (CAiD) enhances self-supervised learning for medical images by using local context. This improves feature discriminability and reusability, overcoming limitations of standard instance discrimination methods.

Keywords:

Instance Discrimination Self-supervised Learning Transfer Learning

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Area of Science:

Medical Imaging
Computer Vision
Machine Learning

Background:

Self-supervised instance discrimination excels in photographic image representation learning.
Instance-based objectives struggle with medical images due to high global anatomical similarity, hindering feature distinctiveness.

Purpose of the Study:

To develop a novel self-supervised framework, Context-Aware instance Discrimination (CAiD), to improve visual representation learning for medical images.
To address the limitations of standard instance discrimination methods in capturing discriminative features from medical data.

Main Methods:

Developed Context-Aware instance Discrimination (CAiD), a self-supervised framework providing finer, discriminative information from local image contexts.
Systematically analyzed learned features for generalizability, transferability, separability, and reusability.

Main Results:

CAiD enriches representations learned by existing instance discrimination methods.
CAiD captures finer contextual information, yielding more discriminative features.
CAiD improves the reusability of low/mid-level features compared to standard methods.

Conclusions:

CAiD effectively enhances self-supervised learning for medical imaging by leveraging local context.
The proposed method offers improved feature representation, discriminability, and reusability for medical downstream tasks.