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Deep convolutional neural networks (CNNs) have been widely used in variousmedical imaging tasks. However, due to the intrinsic locality of convolutionoperation, CNNs generally cannot model long-range dependencies well, which areimportant for accurately identifying or mapping corresponding breast lesionfeatures computed from unregistered multiple mammograms. This motivates us toleverage the architecture of Multi-view Vision Transformers to capturelong-range relationships of multiple mammograms from the same patient in oneexamination. For this purpose, we employ local Transformer blocks to separatelylearn patch relationships within four mammograms acquired from two-view(CC/MLO) of two-side (right/left) breasts. The outputs from different views andsides are concatenated and fed into global Transformer blocks, to jointly learnpatch relationships between four images representing two different views of theleft and right breasts. To evaluate the proposed model, we retrospectivelyassembled a dataset involving 949 sets of mammograms, which include 470malignant cases and 479 normal or benign cases. We trained and evaluated themodel using a five-fold cross-validation method. Without any arduouspreprocessing steps (e.g., optimal window cropping, chest wall or pectoralmuscle removal, two-view image registration, etc.), our four-image(two-view-two-side) Transformer-based model achieves case classificationperformance with an area under ROC curve (AUC = 0.818), which significantlyoutperforms 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 of0.724 (CC view) and 0.769 (MLO view), respectively. The study demonstrates thepotential of using Transformers to develop high-performing computer-aideddiagnosis schemes that combine four mammograms.

Transformers Improve Breast Cancer Diagnosis from Unregistered Multi-View Mammograms