Transformers Improve Breast Cancer Diagnosis from Unregistered Multi-View Mammograms

TL;DR

This paper introduces a Transformer-based model for breast cancer diagnosis from unregistered multi-view mammograms, capturing long-range dependencies across views and sides, leading to improved classification performance over CNNs.

Contribution

The study presents a novel multi-view Transformer architecture that effectively models relationships among unregistered mammograms, outperforming existing CNN-based methods in breast cancer detection.

Findings

Transformer model achieves AUC of 0.818, surpassing CNNs.

Model performs well without complex preprocessing.

Outperforms single-view models significantly.

Abstract

Deep convolutional neural networks (CNNs) have been widely used in various medical imaging tasks. However, due to the intrinsic locality of convolution operation, CNNs generally cannot model long-range dependencies well, which are important for accurately identifying or mapping corresponding breast lesion features computed from unregistered multiple mammograms. This motivates us to leverage the architecture of Multi-view Vision Transformers to capture long-range relationships of multiple mammograms from the same patient in one examination. For this purpose, we employ local Transformer blocks to separately learn patch relationships within four mammograms acquired from two-view (CC/MLO) of two-side (right/left) breasts. The outputs from different views and sides are concatenated and fed into global Transformer blocks, to jointly learn patch relationships between four images representing two different views of the left and right breasts. To evaluate the proposed model, we retrospectively assembled a dataset involving 949 sets of mammograms, which include 470 malignant cases and 479 normal or benign cases. We trained and evaluated the model using a five-fold cross-validation method. Without any arduous preprocessing steps (e.g., optimal window cropping, chest wall or pectoral muscle removal, two-view image registration, etc.), our four-image (two-view-two-side) Transformer-based model achieves case classification performance with an area under ROC curve (AUC = 0.818), which significantly outperforms AUC = 0.784 achieved by the state-of-the-art multi-view CNNs (p = 0.009). It also outperforms two one-view-two-side models that achieve AUC of 0.724 (CC view) and 0.769 (MLO view), respectively. The study demonstrates the potential of using Transformers to develop high-performing computer-aided diagnosis schemes that combine four mammograms.