1 citations · 1 across the 5 of their papers we have counts for
10 papers
Understanding Multilingual Medical ASR Adaptation Through Layer-Wise Analysis
Souranil Kahali, Rituparna Bose, Abner Hernandez +4
Medical automatic speech recognition (MedASR) requires adaptation to specialised terminology, limited annotated clinical data, and multilingual use cases. Although large-scale pret…
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…
Parameter-Efficient Domain Adaptation of Physics-Informed Self-Attention based GNNs for AC Power Flow Prediction
Redwanul Karim, Changhun Kim, Timon Conrad +7
Accurate AC power flow (AC-PF) prediction under domain shift is critical when models trained on medium-voltage (MV) grids are deployed on high-voltage (HV) networks. Existing physi…
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…
VocSegMRI: Multimodal Learning for Precise Vocal Tract Segmentation in Real-time MRI
Daiqi Liu, Johannes Enk, Maureen Stone +7
Accurate segmentation of articulatory structures in real-time MRI (rtMRI) remains challenging, as existing methods rely primarily on visual cues and overlook complementary informat…
Physics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operator
Changhun Kim, Timon Conrad, Redwanul Karim +6
Physics-informed graph neural networks (PIGNNs) have emerged as fast AC power-flow solvers that can replace the classic NewtonRaphson (NR) solvers, especially when thousands of sce…