{"id":2894,"date":"2022-02-23T09:00:30","date_gmt":"2022-02-23T09:00:30","guid":{"rendered":"https:\/\/cvblab.webs.upv.es\/clarify-project.eu\/?p=2894"},"modified":"2022-02-23T16:38:23","modified_gmt":"2022-02-23T16:38:23","slug":"blog-publication-esr8-efficient-cancer-classification","status":"publish","type":"post","link":"https:\/\/cvblab.webs.upv.es\/clarify-project.eu\/blog-publication-esr8-efficient-cancer-classification\/","title":{"rendered":"ESR8 – Efficient Cancer Classification by Coupling Semi Supervised and Multiple Instance Learning"},"content":{"rendered":"

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In this work we couple SSL and MIL to train a deep learning classifier that combines the advantages of both methods and overcomes their limitations.<\/h3>\n

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\n\t\t\tHello there! I\u2019m happy to announce that his first article as a first author was recently published in IEEE Access, a multidisciplinary journal which is Q1 in computer science and engineering.<\/span>\n\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n

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For me, computational pathology was a completely new field which yields some very specific challenges for machine learning algorithms – Whole Slide Images have very high resolutions and labeled data is often scarce. In this article we provide a possible solution for these challenges by coupling multiple instance and semi supervised learning. This allows us to train a machine learning model with a small percentage of labeled data while still maintaining a high performance. To know more, please check out the article<\/span>\u00a0<\/span>https:\/\/ieeexplore.ieee.org\/document\/9681818<\/span><\/a>.<\/span>
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<\/span>The perspective to actually help people with better diagnostic tools is a huge motivation and I am curious about everything yet to come.<\/span>\u00a0<\/span><\/p>\n

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Proposed training framework, combining semi supervised and multiple instance learning for efficient cancer classification. It utilizes the global WSI diagnosis, unlabeled patches and a limited amount of labeled patches for training. The model requires less labels while showing a performance comparable to supervised state-of-the-art methods.<\/span><\/p>\n

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Thanks for the great collaboration to Julio Silva Rodr\u00edguez, Valery Naranjo Ornedo and my supervisor Rafael Molina<\/span>\u00a0<\/span><\/p>\n

Arne Schmidt – ESR8<\/em><\/p>\n

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