Deep Fisher Vector Coding For Whole Slide Image Classification

Adopting machine learning methods for histological sections is a challenging task given the generated huge size of whole slide images (WSIs) especially using high power resolution. In this paper we propose a novel WSI classification method which efficiently predicts a WSI's label. The proposed...

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Bibliographic Details
Main Authors: Akbarnejad, Amir, Ray, Nilanjan, Bigras, Gilbert
Format: Conference Proceeding
Language:English
Subjects:
deep learning
digital pathology
Fisher vector distribution encoding
Image classification
Image coding
Image resolution
Machine learning
Sociology
Statistics
Task analysis
whole slide image classification
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Summary:Adopting machine learning methods for histological sections is a challenging task given the generated huge size of whole slide images (WSIs) especially using high power resolution. In this paper we propose a novel WSI classification method which efficiently predicts a WSI's label. The proposed method considers each WSI as a population of patches and computes a statistic by having some samples from the population. This statistic can be computed efficiently, and our test time on a WSI is about one tenth of that of the existing methods. Moreover, our pooling strategy on the WSI is more general than that of previous works. Further, the assumptions of our method are quite general, and therefore, it is applicable to any WSI classification task. The experiments show that the performance of our method is competitive in two different tasks, while, unlike some of the competing methods, it does not consider any prior clinical knowledge about the label to be predicted.
ISSN:1945-8452
DOI:10.1109/ISBI48211.2021.9433836