Artifact-Robust Graph-Based Learning in Digital Pathology
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Abstract (arXiv)
Whole slide images~(WSIs) are digitized images of tissues placed in glass slides using advanced scanners. The digital processing of WSIs is challenging as they are gigapixel images and stored in multi-resolution format. A common challenge with WSIs is that perturbations/artifacts are inevitable during storing the glass slides and digitizing them. These perturbations include motion, which often arises from slide movement during placement, and changes in hue and brightness due to variations in staining chemicals and the quality of digitizing scanners. In this work, a novel robust learning approach to account for these artifacts is presented. Due to the size and resolution of WSIs and to account for neighborhood information, graph-based methods are called for. We use graph convolutional network~(GCN) to extract features from the graph representing WSI. Through a denoiser {and pooling layer}, the effects of perturbations in WSIs are controlled and the output is followed by a transformer for the classification of different grades of prostate cancer. To compare the efficacy of the proposed approach, the model without denoiser is trained and tested with WSIs without any perturbation and then different perturbations are introduced in WSIs and passed through the network with the denoiser. The accuracy and kappa scores of the proposed model with prostate cancer dataset compared with non-robust algorithms show significant improvement in cancer diagnosis.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 54 references that are in the map. First posted on arXiv 27 Oct 2023.
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References · 24
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12 more
- 2020Learning to Drop: Robust Graph Neural Network via Topological Denoisingcited by 127
- 2021Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networkscited by 124
- 2021DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networkscited by 117
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- 2020Representation Learning of Histopathology Images using Graph Neural Networkscited by 40
- 2022Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathologycited by 26
- 2019Histographs: Graphs in Histopathologycited by 20
- 2021Untrained Graph Neural Networks for Denoisingcited by 15
- 2022From Modern CNNs to Vision Transformers: Assessing the Performance, Robustness, and Classification Strategies of Deep Learning Models in Histopathologycited by 9
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- 2021Reliable Graph Neural Network Explanations Through Adversarial Trainingcited by 3
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