{"doi":"10.48550/arxiv.2302.13631","title":"Curriculum Based Multi-Task Learning for Parkinson's Disease Detection","abstract":"There is great interest in developing radiological classifiers for diagnosis, staging, and predictive modeling in progressive diseases such as Parkinson's disease (PD), a neurodegenerative disease that is difficult to detect in its early stages. Here we leverage severity-based meta-data on the stages of disease to define a curriculum for training a deep convolutional neural network (CNN). Typically, deep learning networks are trained by randomly selecting samples in each mini-batch. By contrast, curriculum learning is a training strategy that aims to boost classifier performance by starting with examples that are easier to classify. Here we define a curriculum to progressively increase the difficulty of the training data corresponding to the Hoehn and Yahr (H&Y) staging system for PD (total N=1,012; 653 PD patients, 359 controls; age range: 20.0-84.9 years). Even with our multi-task setting using pre-trained CNNs and transfer learning, PD classification based on T1-weighted (T1-w) MRI was challenging (ROC AUC: 0.59-0.65), but curriculum training boosted performance (by 3.9%) compared to our baseline model. Future work with multimodal imaging may further boost performance.","journal":"arXiv (Cornell University)","year":2023,"id":403379,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9624,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":810427,"name":"Conor Owens‐Walton","orcid":"0000-0003-0589-638X","position":1,"is_corresponding":false},{"id":1160601,"name":"Emily Laltoo","orcid":null,"position":2,"is_corresponding":false},{"id":312688,"name":"Christina P. Boyle","orcid":"0000-0003-2700-4450","position":3,"is_corresponding":false},{"id":736947,"name":"Yao-Liang Chen","orcid":null,"position":4,"is_corresponding":false},{"id":804567,"name":"Philip J. Cook","orcid":"0000-0001-5094-9052","position":5,"is_corresponding":false},{"id":227422,"name":"Corey T. McMillan","orcid":"0000-0002-7581-6405","position":6,"is_corresponding":false},{"id":1044350,"name":"Chih‐Chien Tsai","orcid":"0000-0003-2210-1972","position":7,"is_corresponding":false},{"id":1181148,"name":"Wang, J-J","orcid":null,"position":8,"is_corresponding":false},{"id":1030667,"name":"Yih‐Ru Wu","orcid":"0000-0003-1191-2542","position":9,"is_corresponding":false},{"id":624118,"name":"Ysbrand D. van der Werf","orcid":"0000-0003-2370-9584","position":10,"is_corresponding":false},{"id":51712,"name":"Paul M. Thompson","orcid":"0000-0002-4720-8867","position":11,"is_corresponding":false},{"id":517575,"name":"Nikhil J. Dhinagar","orcid":"0000-0003-2424-4854","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T01:20:36.280647Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}