2 citations · 2 across the 6 of their papers we have counts for
6 papers
DentalGPT: Incentivizing Multimodal Complex Reasoning in Dentistry
Zhenyang Cai, Jiaming Zhang, Junjie Zhao +21
Reliable interpretation of multimodal data in dentistry is essential for automated oral healthcare, yet current multimodal large language models (MLLMs) struggle to capture fine-gr…
Label tree semantic losses for rich multi-class medical image segmentation
Junwen Wang, Oscar MacCormac, William Rochford +3
Rich and accurate medical image segmentation is poised to underpin the next generation of AI-defined clinical practice by delineating critical anatomy for pre-operative planning, g…
Tree-based Semantic Losses: Application to Sparsely-supervised Large Multi-class Hyperspectral Segmentation
Junwen Wang, Oscar Maccormac, William Rochford +3
Hyperspectral imaging (HSI) shows great promise for surgical applications, offering detailed insights into biological tissue differences beyond what the naked eye can perceive. Ref…
OOD-SEG: Exploiting out-of-distribution detection techniques for learning image segmentation from sparse multi-class positive-only annotations
Junwen Wang, Zhonghao Wang, Oscar MacCormac +2
Despite significant advancements, segmentation based on deep neural networks in medical and surgical imaging faces several challenges, two of which we aim to address in this work.…
Scribble-Based Interactive Segmentation of Medical Hyperspectral Images
Zhonghao Wang, Junwen Wang, Charlie Budd +3
Hyperspectral imaging (HSI) is an advanced medical imaging modality that captures optical data across a broad spectral range, providing novel insights into the biochemical composit…
Cascade Learning Localises Discriminant Features in Visual Scene Classification
Junwen Wang, Katayoun Farrahi
Lack of interpretability of deep convolutional neural networks (DCNN) is a well-known problem particularly in the medical domain as clinicians want trustworthy automated decisions.…