Multi-Magnification Image Search in Digital Pathology.

This paper investigates the effect of magnification on content-based image search in digital pathology archives and proposes to use multi-magnification image representation. Image search in large archives of digital pathology slides provides researchers and medical professionals with an opportunity to match records of current and past patients and learn from evidently diagnosed and treated cases. When working with microscopes, pathologists switch between different magnification levels while examining tissue specimens to find and evaluate various morphological features. Inspired by the conventional pathology workflow, we have investigated several magnification levels in digital pathology and their combinations to minimize the gap between AI-enabled image search methods and clinical settings. The proposed searching framework does not rely on any regional annotation and potentially applies to millions of unlabelled (raw) whole slide images. This paper suggests two approaches for combining magnification levels and compares their performance. The first approach obtains a single-vector deep feature representation for a digital slide, whereas the second approach works with a multi-vector deep feature representation. We report the search results of 20×, 10×, and 5× magnifications and their combinations on a subset of The Cancer Genome Atlas (TCGA) repository. The experiments verify that cell-level information at the highest magnification is essential for searching for diagnostic purposes. In contrast, low-magnification information may improve this assessment depending on the tumor type. Our multi-magnification approach achieved up to 11% F1-score improvement in searching among the urinary tract and brain tumor subtypes compared to the single-magnification image search.

Fine-Tuning and training of densenet for histopathology image representation using TCGA diagnostic slides.

Feature vectors provided by pre-trained deep artificial neural networks have become a dominant source for image representation in recent literature. Their contribution to the performance of image analysis can be improved through fine-tuning. As an ultimate solution, one might even train a deep network from scratch with the domain-relevant images, a highly desirable option which is generally impeded in pathology by lack of labeled images and the computational expense. In this study, we propose a new network, namely 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 1000×1000 pixels acquired at 20× magnification through our proposed "high-cellularity mosaic" approach to enable the usage of weak labels of 7126 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. As well, we designed and trained multiple convolutional batch-normalized ReLU (CBR) networks. The results show that KimiaNet provides superior results compared to the original DenseNet and smaller CBR networks when used as feature extractor to represent histopathology images.