{"doi":"10.1109/access.2023.3289219","title":"DL4ALL: Multi-Task Cross-Dataset Transfer Learning for Acute Lymphoblastic Leukemia Detection","abstract":null,"journal":"IEEE Access","year":2023,"id":606989,"datarank":0.5101796072493234,"base_score":3.4011973816621555,"endowment":3.4011973816621555,"self_citation_contribution":0.5101796072493234,"citation_network_contribution":0.0,"self_endowment_contribution":0.5101796072493234,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":29,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1558444,"name":"Vincenzo Piuri","orcid":"0000-0003-3178-8198","position":1,"is_corresponding":false},{"id":1558445,"name":"Konstantinos N. Plataniotis","orcid":"0000-0003-3647-5473","position":2,"is_corresponding":false},{"id":1558447,"name":"Fabio Scotti","orcid":"0000-0002-4277-3701","position":3,"is_corresponding":false},{"id":1558443,"name":"Angelo Genovese","orcid":"0000-0002-3683-4723","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"DL4ALL: Multi-Task Cross-Dataset Transfer Learning for Acute Lymphoblastic Leukemia Detection","abstract":"Methods for the detection of Acute Lymphoblastic (or Lymphocytic) Leukemia (ALL) are increasingly considering Deep Learning (DL) due to its high accuracy in several fields, including medical imaging. In most cases, such methods use transfer learning techniques to compensate for the limited availability of labeled data. However, current methods for ALL detection use traditional transfer learning, which requires the models to be fully trained on the source domain, then fine-tuned on the target domain, with the drawback of possibly overfitting the source domain and reducing the generalization capability on the target domain. To overcome this drawback and increase the classification accuracy that can be obtained using transfer learning, in this paper we propose our method named “Deep Learning for Acute Lymphoblastic Leukemia” (DL4ALL), a novel multi-task learning DL model for ALL detection, trained using a cross-dataset transfer learning approach. The method adapts an existing model into a multi-task classification problem, then trains it using transfer learning procedures that consider both source and target databases at the same time, interleaving batches from the two domains even when they are significantly different. The proposed DL4ALL represents the first work in the literature using a multi-task cross-dataset transfer learning procedure for ALL detection. Results on a publicly-available ALL database confirm the validity of our approach, which achieves a higher accuracy in detecting ALL with respect to existing methods, even when not using manual labels for the source domain.","is_dataset_classified":null,"base_score":3.4011973816621555,"endowment":3.4011973816621555,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"23304386","pmcid":null,"openalex_id":"https://openalex.org/W4382138864","authors":[],"funders":[{"funder_name":"European Commission (EC) through EdgeAI","grant_id":"101097300","title":"Edge AI Technologies for Optimised Performance Embedded Processing"},{"funder_name":"GLACIATION Projec","grant_id":"101070141","title":"Green responsibLe privACy preservIng dAta operaTIONs"},{"funder_name":"Italian Ministero dell'Università e della Ricerca (MUR) through SERICS Project [National Recovery and Resilience Plan (NRRP) MUR Program by the EU-Next Generation EU (NGEU)]","grant_id":"PE00000014","title":null}],"total_grants":3,"fwci":2.8351,"citation_percentile":0.92253867,"influential_citations":0,"citation_trend":[{"year":2023,"count":2},{"year":2024,"count":8},{"year":2025,"count":13},{"year":2026,"count":6}],"oa_status":"gold","license":"CC BY NC ND","oa_locations":[{"url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10162211.pdf","host_type":"journal"},{"url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10162211.pdf","host_type":"publisher"},{"url":"http://xplorestaging.ieee.org/ielx7/6287639/10005208/10162211.pdf?arnumber=10162211","host_type":"publisher"},{"url":"https://doi.org/10.1109/access.2023.3289219","host_type":"journal"},{"url":"https://hdl.handle.net/2434/978628","host_type":"repository"},{"url":"https://doaj.org/article/b267583b943d47cfbf1a062821d1fb76","host_type":"repository"},{"url":"https://doi.org/10.1109/ACCESS.2023.3289219","host_type":"repository"},{"url":"https://air.unimi.it/bitstream/2434/978628/5/DL4ALL_Multi-Task_Cross-Dataset_Transfer_Learning_for_Acute_Lymphoblastic_Leukemia_Detection%282%29.pdf","host_type":"repository"},{"url":"https://zenodo.org/records/14749172","host_type":""},{"url":"http://dx.doi.org/10.1109/ACCESS.2023.3289219","host_type":""},{"url":"http://dx.doi.org/10.1109/access.2023.3289219","host_type":""}],"fields_of_study":["Digital Imaging for Blood Diseases","COVID-19 diagnosis using AI","Neonatal and Maternal Infections","0202 electrical engineering, electronic engineering, information engineering","02 engineering and technology"],"mesh_terms":[],"keywords":["Transfer of learning","Computer science","Multi-task learning","Artificial intelligence","Overfitting","Machine learning","Deep learning","Generalization","Task (project management)","Inductive transfer","Lymphoblastic Leukemia","Domain (mathematical analysis)","Artificial neural network","Leukemia","Robot learning","deep learning (DL)","convolutional neural networks (CNNs)","Electrical engineering. Electronics. Nuclear engineering","Acute lymphoblastic leukemia (ALL)","Acute Lymphoblastic Leukemia (ALL); Deep Learning (DL); Convolutional Neural Networks (CNN);","TK1-9971"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-30T05:32:42.654958Z","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":[]}