2 citations · 5 across the 15 of their papers we have counts for
16 papers
Bridging Single Distortion Artifacts and Multifactorial Clinical Quality: Few-shot Biparametric MRI Quality Assessment via Distortion-trained Prototypical Networks
Yucheng Tang, Alexander Ng, Wen Yan +11
Clinical prostate multi-parametric MRI relies heavily on high-quality diffusion-weighted imaging (DWI), yet reading DWI is frequently compromised by geometric distortion, often cau…
Radiomics-Integrated Deep Learning with Hierarchical Loss for Osteosarcoma Histology Classification
Yaxi Chen, Zi Ye, Shaheer U. Saeed +4
Osteosarcoma (OS) is an aggressive primary bone malignancy. Accurate histopathological assessment of viable versus non-viable tumor regions after neoadjuvant chemotherapy is critic…
Interpretability and Individuality in Knee MRI: Patient-Specific Radiomic Fingerprint with Reconstructed Healthy Personas
Yaxi Chen, Simin Ni, Shuai Li +6
For automated assessment of knee MRI scans, both accuracy and interpretability are essential for clinical use and adoption. Traditional radiomics rely on predefined features chosen…
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…
TUS-REC2024: A Challenge to Reconstruct 3D Freehand Ultrasound Without External Tracker
Qi Li, Shaheer U. Saeed, Yuliang Huang +24
Trackerless freehand ultrasound reconstruction aims to reconstruct 3D volumes from sequences of 2D ultrasound images without relying on external tracking systems. By eliminating th…
A versatile foundation model for cine cardiac magnetic resonance image analysis tasks
Yunguan Fu, Wenjia Bai, Weixi Yi +7
Here we present a versatile foundation model that can perform a range of clinically-relevant image analysis tasks, including segmentation, landmark localisation, diagnosis, and pro…
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…
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…
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…
T2-Only Prostate Cancer Prediction by Meta-Learning from Bi-Parametric MR Imaging
Weixi Yi, Yipei Wang, Natasha Thorley +6
Current imaging-based prostate cancer diagnosis requires both MR T2-weighted (T2w) and diffusion-weighted imaging (DWI) sequences, with additional sequences for potentially greater…
AI-assisted prostate cancer detection and localisation on biparametric MR by classifying radiologist-positives
Xiangcen Wu, Yipei Wang, Qianye Yang +5
Prostate cancer diagnosis through MR imaging have currently relied on radiologists' interpretation, whilst modern AI-based methods have been developed to detect clinically signific…
SAMReg: SAM-enabled Image Registration with ROI-based Correspondence
Shiqi Huang, Tingfa Xu, Ziyi Shen +4
This paper describes a new spatial correspondence representation based on paired regions-of-interest (ROIs), for medical image registration. The distinct properties of the proposed…