A multi-slice attention fusion and multi-view personalized fusion lightweight network for Alzheimer’s disease diagnosis

The results show that the proposed approach not only excels in resource utilization, but also significantly outperforms the four comparison methods in terms of accuracy and sensitivity, particularly in detecting early-stage AD lesions. It can precisely capture and accurately identify subtle brain lesions, providing crucial technical support for early intervention and treatment.

Authors

Hongshun Cai
Xiaowei Tan
Qiongmin Zhang
Siyi Yu (ORCID: https://orcid.org/0000-0002-3978-8953)
Ying Long
Yin Shi (ORCID: https://orcid.org/0000-0003-3109-6204)

Institutions

Chongqing University of Science and Technology (CN)
Chongqing University of Technology (CN)

Publication Details

Journal: BMC Medical Imaging
Published: 2024-09-27
DOI: https://doi.org/10.1186/s12880-024-01429-8
Citations: 5
Primary Topic: Brain Tumor Detection and Classification
Type: article
Field-Weighted Citation Impact: 1.24

Funders

Natural Science Foundation of Chongqing
Chongqing Municipal Education Commission
Centre Scientifique et Technique du Bâtiment

Controls

|||

ALL TIME

JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

article

A multi-slice attention fusion and multi-view personalized fusion lightweight network for Alzheimer’s disease diagnosis

Hongshun Cai, Xiaowei Tan, Qiongmin Zhang, Siyi Yu et al.

Sep 27, 2024

5 citations

BMC Medical Imaging

Brain Tumor Detection and Classification

1.24

article

A multi-slice attention fusion and multi-view personalized fusion lightweight network for Alzheimer’s disease diagnosis

Hongshun Cai, Xiaowei Tan, Qiongmin Zhang, Siyi Yu, Ying Long, Yin Shi

September 27, 2024article en

5 citations

Abstract

The results show that the proposed approach not only excels in resource utilization, but also significantly outperforms the four comparison methods in terms of accuracy and sensitivity, particularly in detecting early-stage AD lesions. It can precisely capture and accurately identify subtle brain lesions, providing crucial technical support for early intervention and treatment.

BMC Medical ImagingVol. 24(1)

Chongqing University of Science and Technology (CN), Chongqing University of Technology (CN)

Natural Science Foundation of Chongqing, Chongqing Municipal Education Commission, Centre Scientifique et Technique du Bâtiment

Openalex Percentile: Top 20%

Brain Tumor Detection and Classification

1.24

DOI Open Access PDF OpenAlex

AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

A multi-slice attention fusion and multi-view personalized fusion lightweight network for Alzheimer’s disease diagnosis — Hongshun Cai, Xiaowei Tan, et al. · BMC Medical Imaging (2024) | TGRS Research Map | TGRS