ArticleFrontiers in oncology2025
Automatic joint segmentation and classification of breast ultrasound images via multi-task learning with object contextual attention.
Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- DGTN: Graph-Enhanced Transformer with Diffusive Attention and Gating Mechanism for Multi-Task Breast Ultrasound Tumor Analysis.Bioengineering (Basel, Switzerland) · 2026Article
- Classification and Segmentation of Medical Images Using Cross-Representation Attention Fusion and Fuzzy Image Enhancement.Sensors (Basel, Switzerland) · 2026Article
- Efficient Multitask Onboard Vision Sensing for Open-Pit Mining Advanced Driver Assistance System with Classification-Guided Adaptive Temporal Inference.Sensors (Basel, Switzerland) · 2026Article
- Multi-Task and Federated Learning for Breast and Lung Cancer Screening and Diagnosis: A Survey and Future Research Directions.Journal of imaging · 2026Review
- Sequential Transfer Learning for Multi-Domain Breast Image Segmentation Using a Transformer-Enhanced Hybrid U-Net.Bioengineering (Basel, Switzerland) · 2026Article
- The Current Research Landscape on Integrating Artificial Intelligence with Ultrasound Imaging for Cancer Diagnosis: A Dual-Database Bibliometric Study.Current medicinal chemistry · 2026Article
- Breast tumor segmentation and morphological feature-based classification in ultrasound using a two-stage U-net and SVM.Frontiers in bioengineering and biotechnology · 2026Article
- A Dual-stage Deep Learning Framework for Breast Ultrasound Image Segmentation and Classification.Journal of medical systems · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The segmentation and classification of breast ultrasound (BUS) images are crucial for the early diagnosis of breast cancer and remain a key focus in BUS image processing. Numerous machine learning and deep learning algorithms have shown their effectiveness in the segmentation and diagnosis of BUS images. In this work, we propose a multi-task learning network with an object contextual attention module (MTL-OCA) for the segmentation and classification of BUS images. The proposed method utilizes the object contextual attention module to capture pixel-region relationships, enhancing the quality of segmentation masks. For classification, the model leverages high-level features extracted from unenhanced segmentation masks to improve accuracy. Cross-validation on a public BUS dataset demonstrates that MTL-OCA outperforms several current state-of-the-art methods, achieving superior results in both classification and segmentation tasks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.