2 citations · 5 across the 15 of their papers we have counts for
7 papers · 2 filters
Cryo-RL: automating prostate cancer cryoablation planning with reinforcement learning
Trixia Simangan, Ahmed Nadeem Abbasi, Yipeng Hu +1
Cryoablation is a minimally invasive localised treatment for prostate cancer that destroys malignant tissue during de-freezing, while sparing surrounding healthy structures. Its su…
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…
Radiomic fingerprints for knee MR images assessment
Yaxi Chen, Simin Ni, Shaheer U. Saeed +5
Accurate interpretation of knee MRI scans relies on expert clinical judgment, often with high variability and limited scalability. Existing radiomic approaches use a fixed set of r…
SPARS: Self-Play Adversarial Reinforcement Learning for Segmentation of Liver Tumours
Catalina Tan, Yipeng Hu, Shaheer U. Saeed
Accurate tumour segmentation is vital for various targeted diagnostic and therapeutic procedures for cancer, e.g., planning biopsies or tumour ablations. Manual delineation is extr…
Patient-specific radiomic feature selection with reconstructed healthy persona of knee MR images
Yaxi Chen, Simin Ni, Aleksandra Ivanova +5
Classical radiomic features have been designed to describe image appearance and intensity patterns. These features are directly interpretable and readily understood by radiologists…
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…