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.

By Istiak Ahmed, Galib Ahmed, K. Shahriar Sanjid, Md. Tanzim Hossain, Md. Nishan Khan, Md. Misbah Khan, Md. Arifur Rahman, Sheikh Anisul Haque, Sharmin Akhtar Rupa, Mohammed Mejbahuddin Mia, Mahmud Hasan Mostofa Kamal, Md. Mostafa Kamal Sarker, M. Monir Uddin
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.

By Solveig Thrun, Zijun Sun, Suaiba A. Salahuddin, Kristoffer Wickstr{\o}m, Elisabeth Wetzer, Stine Hansen, Robert Jenssen, Michael Kampffmeyer
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.

arXiv AI
Jul 8

Token-Based Dual-view Fusion and Adaptation of Large Vision Models for Breast Cancer Classification

arXiv:2607. 06309v1 Announce Type: cross Abstract: 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.

By Aysan Ghayouri Pirsoltan, Shima Babakordi, Mohammad Reza Mohammadi
arXiv Computer Vision
Sep 23

Neoadjuvant chemotherapy response prediction using pretreatment diffusion and contrast-enhanced magnetic resonance imaging with clinical variables

arXiv:2609.26105v1 Announce Type: cross Abstract: Prediction of pathological complete response before neoadjuvant chemotherapy may facilitate more tailored therapeutic planning for breast cancer pati...

By Pablo Garc\'ia Marcos, Paula Puerta Gonz\'alez, Guillermo Lorenzo, H\'ector G\'omez, Covadonga del Camino, Ad\'an Rodr\'iguez, Ignacio Pel\'aez, Angel Rio-Alvarez, V\'ictor M. Gonz\'alez
arXiv AI
Jul 7

Graph Representation Learning of Longitudinal Medical Imaging Trajectories for Treatment Response Prediction

arXiv:2607. 04912v1 Announce Type: cross Abstract: In patients with breast cancer, pathological complete response (pCR) has been established as a clinically meaningful surrogate marker for long-term outcomes.

By Johannes Kiechle, Richard Osuala, Daniel M. Lang, Stefan M. Fischer, Ivana Jan\'i\v{c}kov\'a, Karim Lekadir, Julia A. Schnabel, Jan C. Peeken