Breast Cancer VLMs: Clinically Practical Vision-Language Train-Inference Models

TL;DR

This paper presents a multi-modal vision-language framework for breast cancer detection that combines mammogram images with clinical text to improve diagnostic accuracy and clinical practicality.

Contribution

It introduces a novel multi-modal approach integrating visual and textual data, outperforming vision transformers in clinical breast cancer detection tasks.

Findings

Superior performance in cancer detection and calcification identification.

Effective fusion of imaging and clinical data enhances diagnostic accuracy.

Practical deployment potential across diverse populations.

Abstract

Breast cancer remains the most commonly diagnosed malignancy among women in the developed world. Early detection through mammography screening plays a pivotal role in reducing mortality rates. While computer-aided diagnosis (CAD) systems have shown promise in assisting radiologists, existing approaches face critical limitations in clinical deployment - particularly in handling the nuanced interpretation of multi-modal data and feasibility due to the requirement of prior clinical history. This study introduces a novel framework that synergistically combines visual features from 2D mammograms with structured textual descriptors derived from easily accessible clinical metadata and synthesized radiological reports through innovative tokenization modules. Our proposed methods in this study demonstrate that strategic integration of convolutional neural networks (ConvNets) with language representations achieves superior performance to vision transformer-based models while handling high-resolution images and enabling practical deployment across diverse populations. By evaluating it on multi-national cohort screening mammograms, our multi-modal approach achieves superior performance in cancer detection and calcification identification compared to unimodal baselines, with particular improvements. The proposed method establishes a new paradigm for developing clinically viable VLM-based CAD systems that effectively leverage imaging data and contextual patient information through effective fusion mechanisms.