3 citations · 8 across the 22 of their papers we have counts for
7 papers · 1 filter
Learning to learn skill assessment for fetal ultrasound scanning
Yipei Wang, Qianye Yang, Lior Drukker +3
Traditionally, ultrasound skill assessment has relied on expert supervision and feedback, a process known for its subjectivity and time-intensive nature. Previous works on quantita…
Impact of Clinical Image Quality on Efficient Foundation Model Finetuning
Yucheng Tang, Pawel Rajwa, Alexander Ng +11
Foundation models in medical imaging have shown promising label efficiency, achieving high performance on downstream tasks using only a fraction of the annotated data otherwise req…
Policy to Assist Iteratively Local Segmentation: Optimising Modality and Location Selection for Prostate Cancer Localisation
Xiangcen Wu, Shaheer U. Saeed, Yipei Wang +2
Radiologists often mix medical image reading strategies, including inspection of individual modalities and local image regions, using information at different locations from differ…
Analysis of Image-and-Text Uncertainty Propagation in Multimodal Large Language Models with Cardiac MR-Based Applications
Yucheng Tang, Yunguan Fu, Weixi Yi +4
Multimodal large language models (MLLMs) can process and integrate information from multimodality sources, such as text and images. However, interrelationship among input modalitie…
Reasoning in machine vision by learning fast and slow thinking
Shaheer U. Saeed, Yipei Wang, Veeru Kasivisvanathan +4
Reasoning is a hallmark of human intelligence, enabling adaptive decision-making in complex unfamiliar scenarios. In contrast, machine intelligence remains bound to training data,…
Promptable cancer segmentation using minimal expert-curated data
Lynn Karam, Yipei Wang, Veeru Kasivisvanathan +3
Automated segmentation of cancer on medical images can aid targeted diagnostic and therapeutic procedures. However, its adoption is limited by the high cost of expert annotations r…
Tell2Reg: Establishing spatial correspondence between images by the same language prompts
Wen Yan, Qianye Yang, Shiqi Huang +6
Spatial correspondence can be represented by pairs of segmented regions, such that the image registration networks aim to segment corresponding regions rather than predicting displ…