6 citations · 17 across the 8 of their papers we have counts for
8 papers
SinkSLOT: Sinkhorn via Sparse Lifted Optimal Transport
Ian Hsieh, Soumya Snigdha Kundu, Tom Vercauteren +1
Entropic optimal transport (EOT) has been shown to offer a computationally tractable approximation to exact optimal transport. However, the standard Sinkhorn-Knopp algorithm has tw…
Redefining Instance Matching: A Unified Framework for Part-Aware Matching in Panoptic Segmentation Evaluation
Erik Großkopf, 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…
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…
Themed Challenges to Solve Data Scarcity in Africa: A Proposition for Increasing Local Data Collection and Integration
Mubaraq Yakubu, Udunna Anazodo, Maruf Adewole +6
In Africa, the scarcity of computational resources and medical datasets remains a major hurdle to the development and deployment of artificial intelligence (AI) tools in clinical s…
Average Calibration Losses for Reliable Uncertainty in Medical Image Segmentation
Theodore Barfoot, Luis C. Garcia-Peraza-Herrera, Samet Akcay +2
Deep neural networks for medical image segmentation are often overconfident, compromising both reliability and clinical utility. In this work, we propose differentiable formulation…
Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS) challenge results
Meritxell Riera-Marin, Sikha O K, Julia Rodriguez-Comas +29
Deep learning (DL) has become the dominant approach for medical image segmentation, yet ensuring the reliability and clinical applicability of these models requires addressing key…
Surgeons vs. Computer Vision: A comparative analysis on surgical phase recognition capabilities
Marco Mezzina, Pieter De Backer, Tom Vercauteren +3
Purpose: Automated Surgical Phase Recognition (SPR) uses Artificial Intelligence (AI) to segment the surgical workflow into its key events, functioning as a building block for effi…
Average Calibration Error: A Differentiable Loss for Improved Reliability in Image Segmentation
Theodore Barfoot, Luis Garcia-Peraza-Herrera, Ben Glocker +1
Deep neural networks for medical image segmentation often produce overconfident results misaligned with empirical observations. Such miscalibration, challenges their clinical trans…