Authors: Yuexi Du, John Onofrey, Nicha C. Dvornek
Abstract: Contrastive Language-Image Pre-training (CLIP) shows promise in medical image
analysis but requires substantial data and computational resources. Due to
these restrictions, existing CLIP applications in medical imaging focus mainly
on modalities like chest X-rays that have abundant image-report data available,
leaving many other important modalities under-explored. Here, we propose the
first adaptation of the full CLIP model to mammography, which presents
significant challenges due to labeled data scarcity, high-resolution images
with small regions of interest, and data imbalance. We first develop a
specialized supervision framework for mammography that leverages its multi-view
nature. Furthermore, we design a symmetric local alignment module to better
focus on detailed features in high-resolution images. Lastly, we incorporate a
parameter-efficient fine-tuning approach for large language models pre-trained
with medical knowledge to address data limitations. Our multi-view and
multi-scale alignment (MaMA) method outperforms state-of-the-art baselines for
three different tasks on two large real-world mammography datasets, EMBED and
RSNA-Mammo, with only 52% model size compared with the largest baseline.
Source: http://arxiv.org/abs/2409.18119v1
