9 papers
BrainIAC: Interactive 3D Brain Lesion Segmentation across Heterogeneous MRI Modalities with Online Adaptation
Wentian Xu, Anthony P Addison, Ziyun Liang +3
Brain lesion segmentation is a fundamental task in medical image analysis, playing a critical role in diagnosis, treatment planning, and longitudinal disease monitoring. Yet existi…
Error Detection for PET/CT Radiology Reports: Domain-Specific vs Large Language Models
Hermione Warr, Harry Anthony, Lilli J Freischem +3
Errors in radiology reports can adversely affect patient treatment, yet automated report quality assurance remains challenging because errors are often subtle and require domain ex…
The Invisible Gorilla Effect in Out-of-distribution Detection
Harry Anthony, Ziyun Liang, Hermione Warr +1
Deep Neural Networks achieve high performance in vision tasks by learning features from regions of interest (ROI) within images, but their performance degrades when deployed on out…
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…
DIsoN: Decentralized Isolation Networks for Out-of-Distribution Detection in Medical Imaging
Felix Wagner, Pramit Saha, Harry Anthony +2
Safe deployment of machine learning (ML) models in safety-critical domains such as medical imaging requires detecting inputs with characteristics not seen during training, known as…
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,…
Evaluating Reliability in Medical DNNs: A Critical Analysis of Feature and Confidence-Based OOD Detection
Harry Anthony, Konstantinos Kamnitsas
Reliable use of deep neural networks (DNNs) for medical image analysis requires methods to identify inputs that differ significantly from the training data, called out-of-distribut…
On the use of Mahalanobis distance for out-of-distribution detection with neural networks for medical imaging
Harry Anthony, Konstantinos Kamnitsas
Implementing neural networks for clinical use in medical applications necessitates the ability for the network to detect when input data differs significantly from the training dat…
Modality Cycles with Masked Conditional Diffusion for Unsupervised Anomaly Segmentation in MRI
Ziyun Liang, Harry Anthony, Felix Wagner +1
Unsupervised anomaly segmentation aims to detect patterns that are distinct from any patterns processed during training, commonly called abnormal or out-of-distribution patterns, w…