{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T06:18:38Z","timestamp":1764656318213,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,8,17]],"date-time":"2024-08-17T00:00:00Z","timestamp":1723852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Xiamen City, Fujian Province, China","award":["3502Z20227199","20KK0234","21H03470"],"award-info":[{"award-number":["3502Z20227199","20KK0234","21H03470"]}]},{"name":"Grant-in-Aid for Scientific Research from the Japanese Ministry for Education, Science, Culture and Sports (MEXT)","award":["3502Z20227199","20KK0234","21H03470"],"award-info":[{"award-number":["3502Z20227199","20KK0234","21H03470"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Post-operative early recurrence (ER) of hepatocellular carcinoma (HCC) is a major cause of mortality. Predicting ER before treatment can guide treatment and follow-up protocols. Deep learning frameworks, known for their superior performance, are widely used in medical imaging. However, they face challenges due to limited annotated data. We propose a multi-task pre-training method using self-supervised learning with medical images for predicting the ER of HCC. This method involves two pretext tasks: phase shuffle, focusing on intra-image feature representation, and case discrimination, focusing on inter-image feature representation. The effectiveness and generalization of the proposed method are validated through two different experiments. In addition to predicting early recurrence, we also apply the proposed method to the classification of focal liver lesions. Both experiments show that the multi-task pre-training model outperforms existing pre-training (transfer learning) methods with natural images, single-task self-supervised pre-training, and DINOv2.<\/jats:p>","DOI":"10.3390\/info15080493","type":"journal-article","created":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T06:41:31Z","timestamp":1724049691000},"page":"493","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Early Recurrence Prediction of Hepatocellular Carcinoma Using Deep Learning Frameworks with Multi-Task Pre-Training"],"prefix":"10.3390","volume":"15","author":[{"given":"Jian","family":"Song","sequence":"first","affiliation":[{"name":"School of Mathematical Sciences, Huaqiao University, Quanzhou 362021, China"},{"name":"College of Information Science and Engineering, Ritsumeikan University, Osaka 567-0817, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haohua","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Ritsumeikan University, Osaka 567-0817, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youwen","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Huaqiao University, Quanzhou 362021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianru","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Ritsumeikan University, Osaka 567-0817, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gan","family":"Zhan","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Ritsumeikan University, Osaka 567-0817, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0768-2193","authenticated-orcid":false,"given":"Rahul Kumar","family":"Jain","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Ritsumeikan University, Osaka 567-0817, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5952-0188","authenticated-orcid":false,"given":"Yen-Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Ritsumeikan University, Osaka 567-0817, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"29","DOI":"10.2147\/JHC.S125396","article-title":"Liver Imaging Reporting and Data System: An expert consensus statement","volume":"4","author":"Elsayes","year":"2017","journal-title":"J. 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DINOv2: Learning Robust Visual Features without Supervision. arXiv, Available online: https:\/\/arxiv.org\/abs\/2304.07193."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/8\/493\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:38:20Z","timestamp":1760110700000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/8\/493"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,17]]},"references-count":31,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["info15080493"],"URL":"https:\/\/doi.org\/10.3390\/info15080493","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2024,8,17]]}}}