arXiv Computer Vision
AlignUS: MRI-Guided Ultrasound Representation Learning for ALS Classification from Tongue Images
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arXiv AI
Sep 17
MINT: Multimodal Imaging-to-Speech Knowledge Transfer for Early Alzheimer's Screening
MINT (Multimodal Imaging-to-Speech Knowledge Transfer) is a three-stage framework that transfers MRI-derived biomarkers to speech representations for early Alzheimer’s screening. An MRI teacher creates a compact embedding space for CN‑versus‑MCI classification, and a residual projection head aligns speech features to this space using a geometric loss, allowing imaging‑free inference. Experiments on ADNI‑4 show that aligned speech matches speech baselines, while multimodal fusion outperforms MRI alone, and ablations highlight dropout regularization and self‑supervised pretraining as key design choices.
arXiv Machine Learning
Jun 9
Multi-View Speech Representation Learning for Parkinson's Disease Detection Using Context-guided Cross-modal Attention
arXiv:2606. 09271v1 Announce Type: cross Abstract: Parkinson's disease (PD) is a progressive neurodegenerative disorder that frequently causes speech impairments associated with hypokinetic dysarthria.
arXiv AI
Sep 3
ORB-SVM : An Innovative Hybrid Framework for Efficient Brain Tumor Detection from MRI Scans
The paper presents ORB-SVM, a hybrid framework that combines the ORB algorithm for feature extraction with a Support Vector Machine for classifying brain tumors in MRI scans. It achieves a 99.5% reduction in data size while preserving key diagnostic features, and reports a 97.5% classification accuracy on the Br35H dataset. This approach offers a resource‑efficient alternative to deep learning models, reducing computational cost and data requirements.
arXiv AI
Jun 16
Unifying Acoustic Features and Text with Multimodal LLMs for Neurodegenerative Screening
arXiv:2606. 14788v1 Announce Type: cross Abstract: Voice-based screening offers a scalable and non-invasive way to assess neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD), but their staging remains challenging due to the difficulty of integrating heterogeneous data.
arXiv AI
Sep 15
Anatomical Grounding and Leakage-Aware Multimodal Contrastive Learning for Alzheimer's Disease Classification from Structural MRI
arXiv:2609.15888v1 Announce Type: cross Abstract: Deep networks trained on structural MRI for Alzheimer's disease (AD) staging often reach reasonable accuracy while attending to anatomically irreleva...
arXiv Machine Learning
Jul 7
OC-Distill: Ontology-aware Contrastive Learning with Cross-Modal Distillation for ICU Risk Prediction
arXiv:2604. 16878v2 Announce Type: replace Abstract: Early prediction of severe clinical deterioration and remaining length of stay can enable timely intervention and better resource allocation in high-acuity settings such as the ICU.