KimiaNet - Training a Histopathology Deep Network from Scratch скачать в хорошем качестве

KimiaNet - Training a Histopathology Deep Network from Scratch

A Talk by Professor Tizhoosh at the Pathology Visions 2020. Deep embeddings, or feature vectors, provided by pre-trained deep artificial neural networks have become a dominant source for image representation in digital pathology. Their contribution to the performance of image analysis can be improved through fine-tuning. One might even train a deep network from scratch with the histopathology images, a highly desirable option which is generally impeded in pathology by lack of labeled images and the computational expense. We propose “KimiaNet†that employs the topology of the DenseNet with four dense blocks, fine-tuned and trained with histopathology images in different configurations. We used more than 240,000 image patches with 1000x1000 pixels acquired at 20x magnification through our proposed “high-cellularity mosaic†approach to enable the usage of weak labels of 7,126 whole slide images of formalin-fixed paraffin-embedded human pathology samples publicly available through The Cancer Genome Atlas (TCGA) repository. We tested KimiaNet using three public datasets, namely TCGA, endometrial cancer images, and colorectal cancer images by evaluating the performance of search and classification when corresponding features of different networks are used for image representation.