Localized Perturbations For Weakly-Supervised Segmentation of Glioma Brain Tumours
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.
Code (0)
등록된 구현이 없습니다.
Tasks
3D ClassificationDiagnosticSuperpixelsWeakly supervised segmentationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Semi-supervised Learning using Denoising Autoencoders for Brain Lesion Detection and Segmentation
The work presented explores the use of denoising autoencoders (DAE) for brain lesion detection, segmentation and false positive reduction. Stacked denoising autoencoders (SDAE) were pre-trained using a large number of un…
DenoisingLesion DetectionSegmentationTransfer LearningUnsupervised Domain Adaptation for Pediatric Brain Tumor Segmentation
Significant advances have been made toward building accurate automatic segmentation models for adult gliomas. However, the performance of these models often degrades when applied to pediatric glioma due to their imaging …
Brain Tumor SegmentationDomain AdaptationSegmentationTumor Segmentation+1WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation
This paper introduces WILDCAT, a deep learning method which jointly aims at aligning image regions for gaining spatial invariance and learning strongly localized features. Our model is trained using only global image lab…
General Classificationimage-classificationImage ClassificationObject Localization+4Weakly-Supervised Learning-Based Feature Localization in Confocal Laser Endomicroscopy Glioma Images
Confocal Laser Endomicroscope (CLE) is a novel handheld fluorescence imaging device that has shown promise for rapid intraoperative diagnosis of brain tumor tissue. Currently CLE is capable of image display only and lack…
Decision MakingDiagnosticImage Segmentationobject-detection+3Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification
Deep learning models have shown their potential for several applications. However, most of the models are opaque and difficult to trust due to their complex reasoning - commonly known as the black-box problem. Some field…
ClassificationDecision MakingSegmentationTumour Classification+1