most citedExplainable Image Classification with Reduced Overconfidence for Tissue Characterisation

2 citations · 3 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CV2025

SAMSA 2.0: Prompting Segment Anything with Spectral Angles for Hyperspectral Interactive Medical Image Segmentation

Alfie Roddan, Tobias Czempiel, Chi Xu +2

We present SAMSA 2.0, an interactive segmentation framework for hyperspectral medical imaging that introduces spectral angle prompting to guide the Segment Anything Model (SAM) usi…

cs.CV20252 cited

Explainable Image Classification with Reduced Overconfidence for Tissue Characterisation

Alfie Roddan, Chi Xu, Serine Ajlouni +3

The deployment of Machine Learning models intraoperatively for tissue characterisation can assist decision making and guide safe tumour resections. For image classification models,…

cs.CV20251 cited

SAMSA: Segment Anything Model Enhanced with Spectral Angles for Hyperspectral Interactive Medical Image Segmentation

Alfie Roddan, Tobias Czempiel, Chi Xu +2

Hyperspectral imaging (HSI) provides rich spectral information for medical imaging, yet encounters significant challenges due to data limitations and hardware variations. We introd…

cs.CV2025

Confidence-Based Annotation Of Brain Tumours In Ultrasound

Alistair Weld, Luke Dixon, Alfie Roddan +3

Purpose: An investigation of the challenge of annotating discrete segmentations of brain tumours in ultrasound, with a focus on the issue of aleatoric uncertainty along the tumour…

cs.CV2025

SurgRIPE challenge: Benchmark of Surgical Robot Instrument Pose Estimation

Haozheng Xu, Alistair Weld, Chi Xu +16

Accurate instrument pose estimation is a crucial step towards the future of robotic surgery, enabling applications such as autonomous surgical task execution. Vision-based methods…