Aruzhan Imasheva

BILATERAL VISION-LANGUAGE FRAMEWORK WITH CROSS-LATERAL ATTENTION FOR MAMMOGRAPHY

Screening mammography is read comparatively: radiologists hold one breast against the other, and several finding categories have no meaning outside that comparison. Most automated pipelines nonetheless encode a single breast at a time. We describe Bilateral MV-CLIP, a contrastive image-text framework in which all four...

A BILATERAL VISION-LANGUAGE FRAMEWORK WITH CROSS-LATERAL

Supervised vs. adversarially pre-trained pseudo-label generators for semi-supervised breast cancer patch classification

Automated breast cancer classification from whole-slide images (WSIs) is hampered by the scarcity of expert-annotated patches and the domain variability inherent to multi-institutional datasets. We propose and compare two semi-supervised frameworks for patch-level tumor classification under limited annotation. The firs...

Operating-Point Collapse Under Normal-Class Source Confounding in Cross-Dataset Breast Ultrasound Classification

Background: Public breast ultrasound datasets are assembled around lesions. Of eight sources examined here, five contain no normal images, and under leave-one-dataset-out evaluation, the normal class is therefore almost perfectly confounded with the acquisition source: with one site held out, 356 of the 358 normal trai...