{"id":4071,"date":"2022-08-15T13:54:14","date_gmt":"2022-08-15T12:54:14","guid":{"rendered":"https:\/\/www.ceessnoek.info\/?p=4071"},"modified":"2022-08-15T13:54:15","modified_gmt":"2022-08-15T12:54:15","slug":"miccai-2022-lifelonger-a-benchmark-for-continual-disease-classification","status":"publish","type":"post","link":"https:\/\/www.ceessnoek.info\/index.php\/miccai-2022-lifelonger-a-benchmark-for-continual-disease-classification\/","title":{"rendered":"MICCAI 2022: LifeLonger: A Benchmark for Continual Disease Classification"},"content":{"rendered":"\n

The MICCAI 2022 cam-ready paper entitled: LifeLonger: A Benchmark for Continual Disease Classification<\/em> by Mohammad Mahdi Derakhshani<\/a>, Ivona Najdenkoska, Tom van Sonsbeek, Xiantong Zhen, Dwarikanath Mahapatra, Marcel Worring and Cees G M Snoek is now available<\/a>. Deep learning models have shown a great effectiveness in recognition of findings in medical images. However, they cannot handle the ever-changing clinical environment, bringing newly annotated medical data from different sources. To exploit the incoming streams of data, these models would benefit largely from sequentially learning from new samples, without forgetting the previously obtained knowledge. In this paper we introduce LifeLonger, a benchmark for continual disease classification on the MedMNIST collection, by applying existing state-of-the-art continual learning methods. In particular, we consider three continual learning scenarios, namely, task and class incremental learning and the newly defined cross-domain incremental learning. Task and class incremental learning of diseases address the issue of classifying new samples without re-training the models from scratch, while cross-domain incremental learning addresses the issue of dealing with datasets originating from different institutions while retaining the previously obtained knowledge. We perform a thorough analysis of the performance and examine how the well-known challenges of continual learning, such as the catastrophic forgetting exhibit themselves in this setting. The encouraging results demonstrate that continual learning has a major potential to advance disease classification and to produce a more robust and efficient learning framework for clinical settings. The code repository, data partitions and baseline results for the complete benchmark are publicly available<\/a>.<\/p>\n\n\n\n

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The MICCAI 2022 cam-ready paper entitled: LifeLonger: A Benchmark for Continual Disease Classification by Mohammad Mahdi Derakhshani, Ivona Najdenkoska, Tom van Sonsbeek, Xiantong Zhen, Dwarikanath Mahapatra, Marcel Worring and Cees G M Snoek is now available. Deep learning models have shown a great effectiveness in recognition of findings in medical images. However, they cannot handle […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-4071","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"\nMICCAI 2022: LifeLonger: A Benchmark for Continual Disease Classification - Cees Snoek<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.ceessnoek.info\/index.php\/miccai-2022-lifelonger-a-benchmark-for-continual-disease-classification\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"MICCAI 2022: LifeLonger: A Benchmark for Continual Disease Classification - Cees Snoek\" \/>\n<meta property=\"og:description\" content=\"The MICCAI 2022 cam-ready paper entitled: LifeLonger: A Benchmark for Continual Disease Classification by Mohammad Mahdi Derakhshani, Ivona Najdenkoska, Tom van Sonsbeek, Xiantong Zhen, Dwarikanath Mahapatra, Marcel Worring and Cees G M Snoek is now available. 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