arXiv Computer Vision
MemoCare: An Interactive Multimodal Mobile System for Automated Cognitive Screening
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arXiv Machine Learning
Oct 16
Task-Centric Multimodal Learning for Cognitive Impairment Detection
The paper introduces a contactless, multimodal screening framework that uses video, audio, and text data collected during structured voice interactions with Amazon Alexa to detect mild cognitive impairment (MCI). It evaluates three conversational tasks—spontaneous speech, comprehension, and recall—to determine which provides the most informative signals, compares unimodal models, and assesses multimodal fusion. Results show that while individual modalities perform best on specific tasks, the multimodal model consistently achieves higher accuracy (up to 0.76) across tasks, indicating that integrated behavioral cues can reliably distinguish MCI from healthy aging.
arXiv Computation and Language
Oct 21
NarraSpace: Multimodal Scene-Grounded Evidence for Picture-Description Dementia Classification
NarraSpace is a multimodal framework that links words and speech times to specific picture elements in picture‑description dementia classification tasks. It retrieves utterances for predefined scene events, summarizes coverage and temporal organization, and uses a frozen language model to contrast event text between Alzheimer’s disease and healthy controls, incorporating transcript surprisal and coverage metrics. On the Alzheimer’s Dementia Recognition through Spontaneous Speech benchmark, a support vector classifier built on these features achieves about 81% accuracy and 91% AUC, while the system preserves source records that map event scores back to utterances, speech intervals, and lexical matches.
arXiv AI
Jun 26
What We are Missing in Multimodal LLM Evaluation?
arXiv:2606. 26348v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can process diverse inputs, e.
arXiv Computation and Language
Sep 28
Why Alzheimer's Speech Screening Fails to Generalize: Bridging the Deployment Gap via Cross-Corpus Evidence Anchoring
The paper examines why speech‑based screening for Alzheimer’s disease fails to generalize across different languages, tasks, and recording protocols. Using a leave‑one‑corpus‑out evaluation on four datasets, it finds that 59 of 70 interpretable speech features show conflicting patterns between healthy controls and cognitive risk groups, with pause, silence, and speech rate being highly protocol‑sensitive. The authors propose a fusion method that combines XLM‑R text baseline scores with evidence anchors, improving mean speaker AUC to 0.785 and worst‑case AUC to 0.615, and emphasize the importance of auditing feature transferability and reporting worst‑case domain robustness.
arXiv Machine Learning
Jun 18
Fair Cognitive Impairment Detection Through Unlearning
arXiv:2606. 18571v1 Announce Type: new Abstract: Mild Cognitive Impairment (MCI) is a medical condition characterized by a noticeable decline in memory, language, or thinking abilities.
arXiv AI
Jul 3
MMIR-TCM: Memory-Integrated Multimodal Inference and Retrieval for TCM Clinical Decision Support
arXiv:2607. 01814v1 Announce Type: new Abstract: Traditional Chinese Medicine (TCM) diagnosis, particularly through tongue inspection, faces persistent challenges in subjectivity and reproducibility.