A novel relational regularization feature selection method for joint regression and classification in ad diagnosis
Open accessAbstract
In this paper, we focus on joint regression and classification for Alzheimer's disease diagnosis and propose a new feature selection method by embedding the relational information inherent in the observations into a sparse multi-task learning framework. Specifically, the relational information includes three kinds of relationships (such as feature-feature relation, response-response relation, and sample-sample relation), for preserving three kinds of the similarity, such as for the features, the response variables, and the samples, respectively. To conduct feature selection, we first formulate the objective function by imposing these three relational characteristics along with an ℓ 2,1-norm regularization term, and further propose a computationally efficient algorithm to optimize the proposed objective function. With the dimension-reduced data, we train two support vector regression models to predict the clinical scores of ADAS-Cog and MMSE, respectively, and also a support vector classification model to determine the clinical label. We conducted extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to validate the effectiveness of the proposed method. Our experimental results showed the efficacy of the proposed method in enhancing the performances of both clinical scores prediction and disease status identification, compared to the state-of-the-art methods.
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Automatic computer-assisted diagnosis reduces errors caused by subjective judgment and provides a standardization tool to improve diagnosis efficiency.
“Automatic computer-assisted diagnosis not only reduces errors caused by subjective judgment [8] but also provides an attractive and meaningful standardization tool [9 -11] to improve the ef fi ciency of diagnosis.”
From DilatedFormer: dilated granularity transformer network for placental maturity grading in ultrasound (2023) · cites this paper as [11]
The cited paper is attached to the sentence’s claim that computer-assisted diagnosis provides a standardization tool to improve diagnostic efficiency; the separate claim about subjective-judgment errors is cited to [8]. The Introduction discusses machine-learning methods for identifying diagnostic labels, and the Classification Results report accuracy, but neither passage addresses standardization or diagnostic efficiency (page numbers unavailable). Searches for subjective judgment and standardization or efficiency found no passage establishing those claims. (LLM, conf=0.73)
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DilatedFormer: dilated granularity transformer network for placental maturity grading in ultrasound
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- Authors
- Zhu, X., Suk, H.-I., Wang, L., Lee, S.-W., & Shen, D.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Korea University (KR)
- Corresponding author
- Lee, Seong-Whan
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2017-05
- Volume
- 38
- Pages
- 205-214
- ISSN
- 1361-8415 (ISSN-L)
- PMC
- PMC4862945
- OpenAlex
- W2175191116
- Cited by
- 224
- References
- 63
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 9 · 2023: 16 · 2022: 20 · 2021: 21 · 2020: 26 · 2019: 29 · 2018: 44 · 2017: 35 · 2016: 9
- FWCI
- 23.83
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Medical Image Analysis (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- UNC Libraries
- Funders
- NIH (EB006733, EB008374, EB009634, MH100217, AG041721, AG042599)
- National Research Foundation of Korea (NRF-2015R1A2A1A05001867)
- National Natural Science Foundation of China (61263035 and 61573270)
- Guangxi Natural Science Foundation (2015GXNSFCB139011)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Keywords
- Alzheimer's disease diagnosis, feature selection, l2,1-norm regularization, support vector regression, support vector machine classification, ADAS-Cog, Mini-Mental State Examination, neuroimaging biomarkers, ADNI dataset
- MeSH terms
- Aged, Algorithms, Alzheimer Disease, Female, Humans, Magnetic Resonance Imaging, Male, Pattern Recognition, Automated, Support Vector Machine
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