Relevance maps: A weakly supervised segmentation method for 3D brain tumours in MRIs.

ABSTRACT: With the increased reliance on medical imaging, Deep convolutional neural networks (CNNs) have become an essential tool in the medical imaging-based computer-aided diagnostic pipelines. However, training accurate and reliable classification models often require large fine-grained annotated datasets. To alleviate this, weakly-supervised methods can be used to obtain local information such as region of interest from global labels. This work proposes a weakly-supervised pipeline to extract Relevance Maps of medical images from pre-trained 3D classification models using localized perturbations. The extracted Relevance Map describes a given region's importance to the classification model and produces the segmentation for the region. Furthermore, we propose a novel optimal perturbation generatio

SUBMITTER: Rajapaksa S 

PROVIDER: S-EPMC10365288 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature