Ultrasound Imaging

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12 papers in the last four weeks, up 140% on the four weeks before. 0.1% of all new papers.

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Adaptive Bidirectional Task Interaction for Joint Segmentation and Classification of Breast Ultrasound
Mar 1, 2026Abdullah Al Shafi, Md Kawsar Mahmud Khan Zunayed, Safin Ahmmed +2Ultrasound Image Segmentation Image Segmentation
Khulna University of Engineering and Technology, Bangladesh · University Clermont Auvergne, France
US-JEPA: A Joint Embedding Predictive Architecture for Ultrasound
Feb 22, 2026Ashwath Radhachandran, Vedrana Ivezić, Shreeram Athreya +2Joint-Embedding Predictive Architecture Ultrasound Imaging
Biomedical Artificial Intelligence Research Lab, University of California, Los Angeles, Los Angeles, United States of America · Medical Informatics, University of California, Los Angeles, Los Angeles, United States of America · Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, United States of America +3
Echo-CoPilot: A Multiple-Perspective Agentic Framework for Reliable Echocardiography Interpretation
Dec 6, 2025Moein Heidari, Ali Mehrabian, Mohammad Amin Roohi +5Medical VQA Echocardiography
The University of British Columbia, Vancouver, BC, Canada · Rutgers Cancer Institute, New Brunswick, NJ, USA
Echo-E $^3$ Net: Efficient Endocardial Spatio-Temporal Network for Ejection Fraction Estimation
Mar 21, 2025Moein Heidari, Afshin Bozorgpour, AmirHossein Zarif-Fakharnia +5Efficient Neural Network Inference Left Ventricular Ejection Fraction Estimation
University of British Columbia, Vancouver, BC, Canada · University of Regensburg, Regensburg, Germany · Iran University of Science and Technology, Tehran, Iran +1
Performance of Machine Learning Classification in Sonomammogram Images using BI-RADS
Nov 14, 2023Malitha Gunawardhana, Norbert ZolekMedical Image Classification Ultrasound Imaging
Auckland Bioengineering Institute University of Auckland New Zealand · Institute of Fundamental Technological Research Polish Academy of Sciences Poland
Self-Supervised Cardiac Phase Detection via Single-Parameter Latent Orbits
Date pendingJohn Bonnici, Matthew Baugh, Aleksandra Kulbaka +3Latent Dynamics Modeling Self-Supervised Learning
Departament of Computing, Imperial College London, UK · Friedrich–Alexander University Erlangen–Nürnberg, DE · Ultromics Ltd, Oxford, UK

Mar 1, 2026, cs.CVJ/K move · Enter open · S save

Adaptive Bidirectional Task Interaction for Joint Segmentation and Classification of Breast Ultrasound

Abdullah Al Shafi, Md Kawsar Mahmud Khan Zunayed, Safin Ahmmed, Sk Imran Hossain+1

Khulna University of Engineering and Technology, Bangladesh · University Clermont Auvergne, France

Ultrasound Image Segmentation Image Segmentation Multi-Task Learning Medical Image Classification Ultrasound Imaging

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Joint lesion segmentation and tissue classification in breast ultrasound are usually trained with a shared encoder, so the two branches stop exchanging information once their decoders separate. That is exactly where boundary detail and semantic evidence are most complementary. The proposed method restores this exchange during decoding and, because its value differs between images, lets the network decide per image how much to keep. A Task Interaction Module (TIM) at each of four decoder levels passes pooled boundary context into the classification representation and modulates decoder channels with class-conditioned priors. An Adaptive Interaction Weighting (AIW) unit then blends interacted and original features with a coefficient computed for each image and level. On BUSI the model reaches 74.19% IoU and 90.60% accuracy, and on BUSI-WHU 86.40% IoU and 95.00% accuracy, ahead of encoder-sharing multi-task, transformer segmentation and decoder-interaction baselines evaluated under the same protocol. The ablation shows that multi-scale context and cross-task exchange are not independent: applied separately they contribute 4.00 points of IoU in total, applied together 6.76. Adding the adaptive blend to task interaction alone raises AUC from 94.41% to 97.31%, indicating that the blend acts primarily on the classification branch. Code: https://github.com/C-loud-Nine/Adaptive-Task-Interaction-BUS.

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Latest papers 96

Adaptive Bidirectional Task Interaction for Joint Segmentation and Classification of Breast Ultrasound

US-JEPA: A Joint Embedding Predictive Architecture for Ultrasound

Echo-CoPilot: A Multiple-Perspective Agentic Framework for Reliable Echocardiography Interpretation

Echo-E3^33Net: Efficient Endocardial Spatio-Temporal Network for Ejection Fraction Estimation

Performance of Machine Learning Classification in Sonomammogram Images using BI-RADS

Self-Supervised Cardiac Phase Detection via Single-Parameter Latent Orbits

Echo-E $^3$ Net: Efficient Endocardial Spatio-Temporal Network for Ejection Fraction Estimation