2 citations · 2 across the 5 of their papers we have counts for
10 papers · 1 filter
Redefining Instance Matching: A Unified Framework for Part-Aware Matching in Panoptic Segmentation Evaluation
Erik GroÃkopf, Soumya Snigdha Kundu, Hendrik Möller +9
The Panoptic Quality (PQ) metric is the standard for jointly evaluating instance and semantic segmentation. However, its original definition relies on a One-to-One matching between…
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…
Instance Awareness of Multi-class Semantic Segmentation Loss Functions
Soumya Snigdha Kundu, Florian Kofler, Marina Ivory +3
Instance-sensitive losses for semantic segmentation such as blob loss and CC loss were designed to address instance imbalance, ensuring small lesions generate the same gradient as…
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.…
UltraFlwr -- An Efficient Federated Surgical Object Detection Framework
Yang Li, Soumya Snigdha Kundu, Maxence Boels +6
Surgical object detection in laparoscopic videos enables real-time instrument identification for workflow analysis and skills assessment, but training robust models such as You Onl…
Longitudinal Vestibular Schwannoma Dataset with Consensus-based Human-in-the-loop Annotations
Navodini Wijethilake, Marina Ivory, Oscar MacCormac +17
Accurate segmentation of vestibular schwannoma (VS) on Magnetic Resonance Imaging (MRI) is essential for patient management but often requires time-intensive manual annotations by…