arXiv AI
Explicitly Aligned Multi-View Breast Cancer Risk Prediction from Variable-Length Screening Histories
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arXiv AI
Jul 14
Longitudinal Multi-View Breast Cancer Risk Prediction
arXiv:2607. 11343v1 Announce Type: cross Abstract: Accurate breast cancer risk prediction from screening mammography is critical for enabling personalized screening intervals and early detection.
arXiv Computer Vision
Sep 23
Mammo-LIFE: Longitudinal Mammographic Imaging and Clinical Feature Enrichment for Post-Radiotherapy Outcome Prediction
arXiv:2609.26443v1 Announce Type: new Abstract: Recent advances in Artificial Intelligence (AI)-powered Computer-Aided Diagnosis (CAD) systems have substantially improved breast cancer screening, dia...
arXiv Machine Learning
Sep 11
Longitudinal Risk Prediction in Mammography with Privileged History Distillation
The paper introduces SEM‑HD, a framework that leverages longitudinal mammography history as privileged information during training to improve risk prediction while requiring only a single current exam at inference. By having a student model predict latent representations of past visits and using teacher supervision from actual longitudinal data, SEM‑HD preserves temporal modeling benefits without needing prior exams at deployment. Experiments on three cohorts and two backbone architectures show consistent gains in long‑horizon AUC and pAUC, especially in low false‑positive‑rate regions, and recover much of the performance gap to full‑history models.
arXiv Computer Vision
5d ago
Integrating AI for Human-Centric Breast Cancer Diagnostics: A Multi-Scale and Multi-View Swin Transformer Framework
The paper presents MSMV‑Swin, a hybrid multi‑scale and multi‑view Swin Transformer framework that integrates the Segment Anything Model to isolate breast lobes and reduce background noise. It captures both localized tumor regions and surrounding tissue context, aligning its outputs with radiologists’ interpretation of mammograms. A hybrid fusion structure is added to maintain robustness when one or more mammogram views are missing during testing.
arXiv Computer Vision
Sep 25
OncoVision: Integrating Mammography and Clinical Data through Attention-Driven Multimodal AI for Enhanced Breast Cancer Diagnosis
OncoVision is a privileged‑information training framework that learns from mammography images and clinical data during training but performs inference using only mammographic images. It employs an attention‑based encoder‑decoder to jointly segment masses, calcifications, axillary findings, and breast tissue, and predicts ten structured clinical features such as BI‑RADS. Two late‑fusion strategies (Independent and Dependent) integrate imaging, radiomic, and clinical information to improve diagnostic precision, and a retrospective multi‑reader study showed higher diagnostic confidence, reduced reading time, and segmentation accuracy comparable to or better than radiologists.
Hugging Face Trending Papers
Jul 7
Token-Based Dual-view Fusion and Adaptation of Large Vision Models for Breast Cancer Classification
Accurate breast cancer classification from mammography requires effective integration of complementary information from craniocaudal (CC) and mediolateral oblique (MLO) views, which provide a more complete characterization of breast abnormalities. However, existing multi-view learning approaches typically rely on feature-level aggregation or single-stage cross-attention, which can entangle view-specific and shared representations and restrict interaction to limited network depths.