Representation Learning of Histopathology Images using Graph Neural Networks | AISC

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Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
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Published on ● Video Link: https://www.youtube.com/watch?v=0hvuDppIKzM


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Duration: 33:28
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Find the recording, slides, and more info at https://ai.science/e/representation-learning-of-histopathology-images-using-graph-neural-networks--dKvmB7GkoU9tPtFyd0fH

Speaker: Mohammed Adnan; Discussion Facilitator: Nabila Abraham


Motivation / Abstract

Representation learning for Whole Slide Images (WSIs) is pivotal in developing image-based systems to achieve higher precision in diagnostic pathology. The authors propose a two-stage framework for WSI representation learning. They use graph neural networks to learn relations among sampled representative patches to aggregate the image information into a single vector representation. They also introduce attention via graph pooling to automatically infer patches with higher relevance. The authors experiment on 1,026 lung cancer WSIs with the 40× magnification from The Cancer Genome Atlas (TCGA) dataset, the largest public repository of histopathology images and achieved state-of-the-art accuracy of 88.8% and AUC of 0.89 on lung cancer sub-type classification by extracting features from a pre-trained DenseNet model. The authors will be presenting this work at CVPR 2020!

What was discussed?
- Modelling multiple instance learning as a graph problem
- Graph pooling with attention
- Adjacency matrix learning


What are the key takeaways?


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