← Back to all news
arXiv AI By Jiawen Kang, Dongrui Han, Lingwei Meng, Jingyan Zhou, Jinchao Li, Xixin Wu, Helen Meng

On the Within-class Variation Issue in Alzheimer's Disease Detection

Read the original on arXiv AI →

The paper addresses the challenge of within-class variation in Alzheimer’s Disease (AD) detection, where individuals with the same diagnosis can show differing levels of cognitive impairment. It introduces two methods—Soft Target Distillation (SoTD) and Instance-level Re-balancing (InRe)—that estimate sample-specific AD probabilities to better capture this heterogeneity. Experiments on the ADReSS and CU-MARVEL datasets demonstrate that these scores correlate with independent cognitive assessments and enhance AD detection performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
Sep 2

Cleaner Speech, Weaker Generalization: Revisiting Pitt-Derived Benchmarks for Alzheimer's Disease Detection

The study examines how speech preprocessing—such as enhancement, sample selection, and demographic balancing—affects Alzheimer’s disease detection models that use the Pitt Corpus. Experiments reveal that while speech‑enhanced datasets boost in‑domain accuracy, they diminish cross‑dataset robustness and introduce class imbalance and prediction shifts, even when training and testing enhancements are matched. Large audio‑language models show similar sensitivity, indicating that cleaner speech does not guarantee better real‑world performance.

By Luqi Sun, Shreeram Suresh Chandra, Lin Zhang, You-Jin Li, Brian MacWhinney, Yu Tsao, Emily Mower Provost, Berrak Sisman
llmsbenchmarks
More like this →
arXiv Machine Learning
Oct 12

Acoustic Biomarkers for Alzheimer's Disease Detection from Spontaneous Speech

The study explores acoustic biomarkers derived from spontaneous speech to differentiate Alzheimer's disease patients from healthy controls. Using a structured picture‑description task, low‑level prosodic, spectral, and temporal features were extracted and fed into various machine learning classifiers. The best models achieved clinically meaningful separation between diagnostic groups, indicating that non‑invasive audio screening could aid early detection of dementia.

By Anh L Tran, Arjee Javellana Restar, Bereket Kindo
multimodal
More like this →
Hugging Face Trending Papers
Jun 4

Multilingual Detection of Alzheimer's Disease from Speech: A Cross-Linguistic Transfer Learning Approach

The development of multilingual Alzheimer's Disease Dementia (AD) detection models presents significant challenges due to the resource-intensive and time-consuming nature of language-specific model training. We propose a novel solution using cross-language training to detect AD in languages beyond those used for model training.

llmsfine-tuning
More like this →