1 citations · 1 across the 3 of their papers we have counts for
7 papers
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…
Adapting Self-Supervised Speech Representations for Cross-lingual Dysarthria Detection in Parkinson's Disease
Abner Hernandez, Eunjung Yeo, Kwanghee Choi +12
The limited availability of dysarthric speech data makes cross-lingual detection an important but challenging problem. A key difficulty is that speech representations often encode…
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…
SpeechCT-CLIP: Distilling Text-Image Knowledge to Speech for Voice-Native Multimodal CT Analysis
Lukas Buess, Jan Geier, David Bani-Harouni +6
Spoken communication plays a central role in clinical workflows. In radiology, for example, most reports are created through dictation. Yet, nearly all medical AI systems rely excl…
Differential privacy enables fair and accurate AI-based analysis of speech disorders while protecting patient data
Soroosh Tayebi Arasteh, Mahshad Lotfinia, Paula Andrea Perez-Toro +6
Speech pathology has impacts on communication abilities and quality of life. While deep learning-based models have shown potential in diagnosing these disorders, the use of sensiti…
Impact of Data Sparsity on Machine Learning for Fault Detection in Power System Protection
Julian Oelhaf, Georg Kordowich, Changhun Kim +4
Germany's transition to a renewable energy-based power system is reshaping grid operations, requiring advanced monitoring and control to manage decentralized generation. Machine le…