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The success of supervised deep learning heavily depends on large labeled datasets whose construction is often challenging in medical image analysis. Contrastive learning, a variant of self-supervised learning, is a potential solution to alleviate the strong demand for data annotation. In this work, we extend the contrastive learning framework to 3D volumetric medical imaging.


This paper presents a soft-label anonymous gastric X-ray image distillation method based on a gradient descent approach. The sharing of medical data is demanded to construct high-accuracy computer-aided diagnosis (CAD) systems. However, the large size of the medical dataset and privacy protection are remaining problems in medical data sharing, which hindered the research of CAD systems. The idea of our distillation method is to extract the valid information of the medical dataset and generate a tiny distilled dataset that has a different data distribution.


The blood smear analysis provides vital information and forms the basis to diagnose most of the diseases. With recent developments, deep learning methods can analyze the microscopic blood sample using image processing and classification tasks with less human effort and increased accuracy.


Various protocols have been developed to improve the success rate of In Vitro Fertilization (IVF). Earlier protocols were based on embryonic cell quality on embryos' third day. Newer protocols rely on the blastocyst quality (day-5 embryo).
Artificial intelligence (AI) systems for automatic human embryo quality assessment seem to be the natural trend towards improving IVF's outcome. AI systems can potentially reveal hidden relationships between embryos' various attributes. To this date, most AI systems assess single blastocyst images.