Document detail
ID
oai:arXiv.org:2403.12719
Topic
Computer Science - Machine Learnin...Author
Aviles-Rivero, Angelica I. Cheng, Chun-Wun Deng, Zhongying Kourtzi, Zoe Schönlieb, Carola-BibianeCategory
Computer Science
Year
2024
listing date
3/27/2024
Keywords
introduce multi-modal alzheimer hypergraphAbstract
Early detection of Alzheimer's disease's precursor stages is imperative for significantly enhancing patient outcomes and quality of life.
This challenge is tackled through a semi-supervised multi-modal diagnosis framework.
In particular, we introduce a new hypergraph framework that enables higher-order relations between multi-modal data, while utilising minimal labels.
We first introduce a bilevel hypergraph optimisation framework that jointly learns a graph augmentation policy and a semi-supervised classifier.
This dual learning strategy is hypothesised to enhance the robustness and generalisation capabilities of the model by fostering new pathways for information propagation.
Secondly, we introduce a novel strategy for generating pseudo-labels more effectively via a gradient-driven flow.
Our experimental results demonstrate the superior performance of our framework over current techniques in diagnosing Alzheimer's disease.
Aviles-Rivero, Angelica I.,Cheng, Chun-Wun,Deng, Zhongying,Kourtzi, Zoe,Schönlieb, Carola-Bibiane, 2024, Bilevel Hypergraph Networks for Multi-Modal Alzheimer's Diagnosis
SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models
methods salience efficiency quantization language slim-llm llms
Prediction Models for Suicide Attempts among Adolescents Using Machine Learning Techniques
using data ml prediction 0 suicide adolescents
