collaborators

6 papers

cs.CV2026

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…

cs.LG2026

AEGIS: An Operational Infrastructure for Post-Market Governance of Adaptive Medical AI Under US and EU Regulations

Fardin Afdideh, Mehdi Astaraki, Fernando Seoane +1

Machine learning systems deployed in medical devices require governance frameworks that ensure safety while enabling continuous improvement. Regulatory bodies including the FDA and…

cs.AI2025

Leveraging Imperfection with MEDLEY A Multi-Model Approach Harnessing Bias in Medical AI

Farhad Abtahi, Mehdi Astaraki, Fernando Seoane

Bias in medical artificial intelligence is conventionally viewed as a defect requiring elimination. However, human reasoning inherently incorporates biases shaped by education, cul…

cs.IR2025

ChEmbed: Enhancing Chemical Literature Search Through Domain-Specific Text Embeddings

Ali Shiraee Kasmaee, Mohammad Khodadad, Mehdi Astaraki +4

Retrieval-Augmented Generation (RAG) systems in chemistry heavily depend on accurate and relevant retrieval of chemical literature. However, general-purpose text embedding models f…

cs.CV2025

BrainLesion Suite: A Flexible and User-Friendly Framework for Modular Brain Lesion Image Analysis

Florian Kofler, Marcel Rosier, Mehdi Astaraki +26

BrainLesion Suite is a versatile toolkit for building modular brain lesion image analysis pipelines in Python. Following Pythonic principles, BrainLesion Suite is designed to provi…

eess.IV2025

BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis

Florian Kofler, Marcel Rosier, Mehdi Astaraki +34

The Brain Tumor Segmentation (BraTS) cluster of challenges has significantly advanced brain tumor image analysis by providing large, curated datasets and addressing clinically rele…