7 papers · 1 filter
LocBAM: Advancing 3D Patch-Based Image Segmentation by Integrating Location Contex
Donnate Hooft, Stefan M. Fischer, Cosmin Bercea +2
Patch-based methods are widely used in 3D medical image segmentation to address memory constraints in processing high-resolution volumetric data. However, these approaches often ne…
Influence of Classification Task and Distribution Shift Type on OOD Detection in Fetal Ultrasound
Chun Kit Wong, Anders N. Christensen, Cosmin I. Bercea +3
Reliable out-of-distribution (OOD) detection is important for safe deployment of deep learning models in fetal ultrasound amidst heterogeneous image characteristics and clinical se…
Knowledge to Sight: Reasoning over Visual Attributes via Knowledge Decomposition for Abnormality Grounding
Jun Li, Che Liu, Wenjia Bai +4
In this work, we address the problem of grounding abnormalities in medical images, where the goal is to localize clinical findings based on textual descriptions. While generalist V…
Semantic Alignment of Unimodal Medical Text and Vision Representations
Maxime Di Folco, Emily Chan, Marta Hasny +2
General-purpose AI models, particularly those designed for text and vision, demonstrate impressive versatility across a wide range of deep-learning tasks. However, they often under…
Enhancing Abnormality Grounding for Vision Language Models with Knowledge Descriptions
Jun Li, Che Liu, Wenjia Bai +3
Visual Language Models (VLMs) have demonstrated impressive capabilities in visual grounding tasks. However, their effectiveness in the medical domain, particularly for abnormality…
A Self-Supervised Image Registration Approach for Measuring Local Response Patterns in Metastatic Ovarian Cancer
Inês P. Machado, Anna Reithmeir, Fryderyk Kogl +13
High-grade serous ovarian carcinoma (HGSOC) is characterised by significant spatial and temporal heterogeneity, typically manifesting at an advanced metastatic stage. A major chall…