Sparse capsule networks for informative representation learning in digital pathology
MEDICAL IMAGING 2022: DIGITAL AND COMPUTATIONAL PATHOLOGY(2022)
摘要
Digital pathology involves the digitization of high quality tissue biopsies on microscope slides to be used by physicians for patient diagnosis and prognosis. These slides have become exciting avenues for deep learning applications to improve care. Despite this, labels are difficult to produce and thus remain rare. In this work, we create a sparse capsule network with a spatial broadcast decoder to perform representation learning on segmented nuclei patches extracted from the BreastPathQ dataset. This was able to produce disentangled latent space for categories such as rotations, and logistic regression classifiers trained on the latent space performed well.
更多查看译文
关键词
sparse capsule networks,informative representation learning,digital pathology
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要