← Back to all news
arXiv Machine Learning By Zheng Yu, Xinhang Li, Jiabao Gao, Boyang Wang, Xiang Li

M3D-Net: Hierarchical Coordination of Spatial Context, Feature Reuse, and Differential Attention for Mammography Classification

Read the original on arXiv Machine Learning →

M3D‑Net is a mammography encoder that hierarchically coordinates multi‑scale coordinate attention, bounded dynamic feature reuse, and differential attention through resolution‑aware operator placement. It preserves earlier features within stages, integrates local and global context via coordinate‑aware aggregation, and applies differential attention at coarse resolutions. In image‑only classification on AISSLab mammography and an adapted image‑clinical model on BrEaST ultrasound, M3D‑Net achieves the highest validation accuracy and lowest endpoint cross‑entropy loss compared to EdgeNeXt, RepViT, and TransXNet, with accuracies of 97.78% and 80.39% respectively.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

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
llmscomputer-visionbenchmarks
More like this →
arXiv AI
Aug 18

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.

By Alibek Kamiluly, Milana Muratova, Yash Patel, Fan Li
llmscomputer-vision
More like this →
arXiv AI
Sep 17

A Lightweight CNN Integrated Compact Convolutional Transformer for Multi-Scale Feature Learning and reducing computational complexity for breast cancer mammography image detection and classification

The paper presents a lightweight CNN‑integrated Compact Convolutional Transformer (CCT) designed for multi‑scale feature learning in breast cancer mammography. With only 250,435 parameters, the model achieved 99‑100% accuracy across three datasets using 5‑fold cross‑validation, demonstrating robust generalization. Explainable AI components were added to clarify the classification process, aiming to increase clinical trust in resource‑constrained settings.

By Md Taimur Ahad (Department of Management North South University, Dhaka, Bangladesh), Ainuddin Ahmed (Department of Management North South University, Dhaka, Bangladesh)
llmsragnlp
More like this →
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
computer-visionmultimodal
More like this →