Gradually Applying Weakly Supervised and Active Learning for Mass Detection in Breast Ultrasound Images

TL;DR

This paper introduces a combined weakly supervised and active learning approach to improve mass detection in breast ultrasound images, significantly reducing annotation effort while enhancing localization accuracy.

Contribution

It presents a novel two-stage detection method with controlled weighting for weakly annotated data and an active learning scheme for improving annotations, applicable beyond breast ultrasound images.

Findings

24% increase in correct localization (CorLoc) with controlled weighting.

Additional CorLoc improvement through active learning.

Method effective on Stanford Dog dataset for generalization.

Abstract

We propose a method for effectively utilizing weakly annotated image data in an object detection tasks of breast ultrasound images. Given the problem setting where a small, strongly annotated dataset and a large, weakly annotated dataset with no bounding box information are available, training an object detection model becomes a non-trivial problem. We suggest a controlled weight for handling the effect of weakly annotated images in a two stage object detection model. We~also present a subsequent active learning scheme for safely assigning weakly annotated images a strong annotation using the trained model. Experimental results showed a 24\% point increase in correct localization (CorLoc) measure, which is the ratio of correctly localized and classified images, by assigning the properly controlled weight. Performing active learning after a model is trained showed an additional increase in CorLoc. We tested the proposed method on the Stanford Dog datasets to assure that it can be applied to general cases, where strong annotations are insufficient to obtain resembling results. The presented method showed that higher performance is achievable with lesser annotation effort.