7 papers
A Neurosymbolic Framework for Interpretable Skeleton-Based Seizure Detection via Concept-Driven Logical Reasoning
Talha Ilyas, Deval Mehta, Zongyuan Ge
Video-based seizure detection is essential for the management of epilepsy patients, offering a non-invasive complement to electroencephalography. While several deep learning approa…
Clinically Aligned Geometry Constraints for Robust IVUS Vessel Boundary Segmentation
Yunshu Chen, Litao Yang, Giuseppe Di Giovanni +9
Intravascular ultrasound (IVUS) lumen and external elastic membrane (EEM) segmentation is important for quantitative coronary plaque burden assessment. Errors in lumen or EEM delin…
NeRD: Neuro-Symbolic Rule Distillation for Efficient Ontology-Grounded Chain-of-Thought in Medical Image Diagnosis
Hongxi Yang, Yiwen Jiang, Siyuan Yan +8
Interpretability is essential for trustworthy medical image diagnosis. However, existing concept-driven interpretable methods have key limitations: Concept Bottleneck Models (CBMs)…
Neurosymbolic Framework for Concept-Driven Logical Reasoning in Skeleton-Based Human Action Recognition
Talha Ilyas, Deval Mehta, Zongyuan Ge
Skeleton-based human activity recognition has achieved strong empirical performance, yet most existing models remain black boxes and difficult to interpret. In this work, we introd…
Towards Objective Obstetric Ultrasound Assessment: Contrastive Representation Learning for Fetal Movement Detection
Talha Ilyas, Duong Nhu, Allison Thomas +13
Accurate fetal movement (FM) detection is essential for assessing prenatal health, as abnormal movement patterns can indicate underlying complications such as placental dysfunction…
WISE: Weak-Supervision-Guided Step-by-Step Explanations for Multimodal LLMs in Image Classification
Yiwen Jiang, Deval Mehta, Siyuan Yan +3
Multimodal Large Language Models (MLLMs) have shown promise in visual-textual reasoning, with Multimodal Chain-of-Thought (MCoT) prompting significantly enhancing interpretability.…