Localized Perturbations For Weakly-Supervised Segmentation of Glioma Brain Tumours

• URL: http://arxiv.org/abs/2111.14953v1
• Date: Mon, 29 Nov 2021 21:01:20 GMT
• Title: Localized Perturbations For Weakly-Supervised Segmentation of Glioma Brain Tumours
• Authors: Sajith Rajapaksa and Farzad Khalvati
• Abstract summary: This work proposes the use of localized perturbations as a weakly-supervised solution to extract segmentation masks of brain tumours from a pretrained 3D classification model. We also propose a novel optimal perturbation method that exploits 3D superpixels to find the most relevant area for a given classification using a U-net architecture.
• Score: 0.5801621787540266
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep convolutional neural networks (CNNs) have become an essential tool in the medical imaging-based computer-aided diagnostic pipeline. However, training accurate and reliable CNNs requires large fine-grain annotated datasets. To alleviate this, weakly-supervised methods can be used to obtain local information from global labels. This work proposes the use of localized perturbations as a weakly-supervised solution to extract segmentation masks of brain tumours from a pretrained 3D classification model. Furthermore, we propose a novel optimal perturbation method that exploits 3D superpixels to find the most relevant area for a given classification using a U-net architecture. Our method achieved a Dice similarity coefficient (DSC) of 0.44 when compared with expert annotations. When compared against Grad-CAM, our method outperformed both in visualization and localization ability of the tumour region, with Grad-CAM only achieving 0.11 average DSC.

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