Ultrasound Image
Ultrasound image analysis focuses on extracting meaningful information from ultrasound scans for medical diagnosis and treatment. Current research emphasizes developing robust deep learning models, including convolutional neural networks (CNNs), transformers, and generative adversarial networks (GANs), often combined in hybrid architectures, to improve image segmentation, classification, and noise reduction. These advancements aim to enhance diagnostic accuracy, particularly in areas with limited expert access, and facilitate automated tasks like lesion detection and report generation, ultimately improving patient care and workflow efficiency. The field is also actively exploring explainable AI (XAI) techniques to increase the transparency and trustworthiness of these powerful algorithms.
Papers
Towards Predicting Fine Finger Motions from Ultrasound Images via Kinematic Representation
Dean Zadok, Oren Salzman, Alon Wolf, Alex M. Bronstein
A Human-Centered Machine-Learning Approach for Muscle-Tendon Junction Tracking in Ultrasound Images
Christoph Leitner, Robert Jarolim, Bernhard Englmair, Annika Kruse, Karen Andrea Lara Hernandez, Andreas Konrad, Eric Su, Jörg Schröttner, Luke A. Kelly, Glen A. Lichtwark, Markus Tilp, Christian Baumgartner
Explainable Ensemble Machine Learning for Breast Cancer Diagnosis based on Ultrasound Image Texture Features
Alireza Rezazadeh, Yasamin Jafarian, Ali Kord
Towards deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via Ultrasound Images
Mahmood Alzubaidi, Marco Agus, Khalid Alyafei, Khaled A Althelaya, Uzair Shah, Alaa Abd-Alrazaq, Mohammed Anbar, Michel Makhlouf, Mowafa Househ