From the 1 of 6 linked papers with an AI index.
6 papers
Motion-Conditioned Multi-View Fusion for Myocardial Infarction Localization from Echocardiography
Guang Yang, Wentian Xu, Siyu Wang +3
The paper introduces MCF-Net, a motion-guided multi-view fusion framework that combines sparse motion cues with a pretrained Echo foundation model to locate myocardial infarction s…
You Point, I Learn: Online Adaptation of Interactive Segmentation Models for Handling Distribution Shifts in Medical Imaging
Wentian Xu, Ziyun Liang, Harry Anthony +4
Interactive segmentation uses real-time user inputs, such as mouse clicks, to iteratively refine model predictions. Although not originally designed to address distribution shifts,…
IterMask3D: Unsupervised Anomaly Detection and Segmentation with Test-Time Iterative Mask Refinement in 3D Brain MR
Ziyun Liang, Xiaoqing Guo, Wentian Xu +5
Unsupervised anomaly detection and segmentation methods train a model to learn the training distribution as `normal'. In the testing phase, they identify patterns that deviate from…
Modality-Agnostic Input Channels Enable Segmentation of Brain lesions in Multimodal MRI with Sequences Unavailable During Training
Anthony P. Addison, Felix Wagner, Wentian Xu +2
Segmentation models are important tools for the detection and analysis of lesions in brain MRI. Depending on the type of brain pathology that is imaged, MRI scanners can acquire mu…
Specialised or Generic? Tokenization Choices for Radiology Language Models
Hermione Warr, Wentian Xu, Harry Anthony +3
The vocabulary used by language models (LM) - defined by the tokenizer - plays a key role in text generation quality. However, its impact remains under-explored in radiology. In th…
Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalities
Felix Wagner, Wentian Xu, Pramit Saha +7
Segmentation models for brain lesions in MRI are typically developed for a specific disease and trained on data with a predefined set of MRI modalities. Such models cannot segment…