The application of self-supervised learning tools in the analysis of medical images through the image context restoration approach
سال انتشار: 1402
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 98
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شناسه ملی سند علمی:
ECMCONF08_021
تاریخ نمایه سازی: 3 مهر 1402
چکیده مقاله:
Machine learning, especially deep learning, has enhanced medical image analysis over the past years. Training a good deep learning model requires a large amount of labeled data. Therefore, increasing the performance of machine learning models using unlabeled as well as labeled data is an important but challenging problem. Self-monitored learning offers a possible solution to this problem. In this paper, in order to better exploit unlabeled images, we propose a novel self-supervised learning strategy based oncontext restoration. We validate the context restoration strategy with three common problems in medical imaging: classification, localization, and segmentation. To perform the classification, we apply and test it on the scanning screen of two-dimensional ultrasound images of the fetus; To localize abdominal organs, we apply and test it in CT images, and to divide brain tumors, we apply and test it in multimodal MR images. In all three cases, self-supervised learning based on context restoration learns useful semantic features and leads to improved machine learning models of the above tasks.
کلیدواژه ها:
نویسندگان
Sara Abdollahi Nohooji
Electrical and computer department Azad University, Najaf Abad branch Najafabad, Isfahan