most citedPerceptual implications of automatic anonymization in pathological speech

1 citations · 1 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CV2026

LoGSAM: Parameter-Efficient Cross-Modal Grounding for MRI Segmentation

Mohammad Robaitul Islam Bhuiyan, Sheethal Bhat, Melika Qahqaie +4

Precise localization and delineation of brain tumors using magnetic resonance imaging (MRI) are essential for planning therapy and guiding surgical decisions. To address this, we p…

eess.AS20261 cited

Perceptual implications of automatic anonymization in pathological speech

Soroosh Tayebi Arasteh, Saba Afza, Tri-Thien Nguyen +11

Automatic anonymization is increasingly used to enable ethical sharing of clinical speech, yet its perceptual and clinical consequences remain undercharacterized. We present a huma…

cs.CL2026

Safety and accuracy follow different scaling laws in clinical large language models

Sebastian Wind, Tri-Thien Nguyen, Jeta Sopa +9

Clinical LLMs are often scaled by increasing model size, context length, retrieval complexity, or inference-time compute, with the implicit expectation that higher accuracy implies…

cs.LG2026

Agentic retrieval-augmented reasoning reshapes collective reliability under model variability in radiology question answering

Mina Farajiamiri, Jeta Sopa, Saba Afza +9

Agentic retrieval-augmented reasoning pipelines are increasingly used to structure how large language models (LLMs) incorporate external evidence in clinical decision support. Thes…

cs.CL2025

Multi-step retrieval and reasoning improves radiology question answering with large language models

Sebastian Wind, Jeta Sopa, Daniel Truhn +9

Clinical decision-making in radiology increasingly benefits from artificial intelligence (AI), particularly through large language models (LLMs). However, traditional retrieval-aug…

cs.CV2025

Exemplar Med-DETR: Toward Generalized and Robust Lesion Detection in Mammogram Images and beyond

Sheethal Bhat, Bogdan Georgescu, Adarsh Bhandary Panambur +8

Detecting abnormalities in medical images poses unique challenges due to differences in feature representations and the intricate relationship between anatomical structures and abn…