arXiv AI August 18, 2026 By Alibek Kamiluly, Milana Muratova, Yash Patel, Fan Li

DualMiT-Net: Local-Global Transformer-Convolutional Fusion for Breast Mass Segmentation in Mammographic Regions of Interest

arXiv:2608. 15019v1 Announce Type: cross Abstract: Breast mass segmentation is an important step in computer-aided mammography, but it remains difficult because masses can have low contrast, irregular shapes, and boundaries that blend with surrounding breast tissue.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 14

BiLoG-Net: A Bi-Context Location-Guided Network for Breast Mass Segmentation and Malignancy Classification in Mammography

arXiv:2607. 10188v1 Announce Type: cross Abstract: Breast cancer remains the most commonly diagnosed malignancy among women worldwide, yet accurate detection and characterization of breast masses in mammography remain challenging due to subtle intensity variations, heterogeneous tissue densities, and indistinct lesion boundaries that complicate radiological interpretation.

By Abu Fatema Mohammad Abdun Noor, Md Imam Ahasan, Md Samiul Ahasan, Kah Ong Michael Goh, S M Hasan Mahmud, Raihana Zannat

llms computer-vision benchmarks

arXiv AI

Jun 9

AMN: An Adaptive Multi-Scale Fusion Network with Boundary and Uncertainty Modeling for Nuclei Segmentation

arXiv:2606. 07633v1 Announce Type: cross Abstract: Accurate classification of nuclei subtypes in histopathology images is critical for downstream tasks including tumor grading, immune infiltrate quantification, and prognosis prediction.

By Spoorthi M, Suja Palaniswamy

llms computer-vision benchmarks

arXiv Machine Learning

1d ago

MagViT: Interpretable Multi-Magnification Transformers with Patient-Level Model Selection for Breast Histopathology

arXiv:2608. 16959v1 Announce Type: cross Abstract: Breast cancer is one of the most common types of cancer among women around the world.

By Nabil Ashab, Soumit Kumar Kundu, Saif Mahmud Parvez, Shahadat Hossain Sohag, Bidhan Biswas, Nazmus Subha

llms computer-vision

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

llms computer-vision benchmarks

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.

llms computer-vision benchmarks

arXiv AI

Aug 5

Externally Validated Breast Ultrasound Segmentation via Multi-task Learning with BI-RADS-Consistent Morphological Priors

arXiv:2511. 15968v2 Announce Type: replace-cross Abstract: External validation of breast ultrasound segmentation models remains limited because internal train--test splits do not capture domain shifts across imaging systems, acquisition protocols, and patient populations.

By Jingru Zhang, Saed Moradi, Ashirbani Saha

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Related stories

BiLoG-Net: A Bi-Context Location-Guided Network for Breast Mass Segmentation and Malignancy Classification in Mammography

AMN: An Adaptive Multi-Scale Fusion Network with Boundary and Uncertainty Modeling for Nuclei Segmentation

MagViT: Interpretable Multi-Magnification Transformers with Patient-Level Model Selection for Breast Histopathology

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

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

Externally Validated Breast Ultrasound Segmentation via Multi-task Learning with BI-RADS-Consistent Morphological Priors