Understanding and Interpreting Machine Learning in Medical Image Computing Applications

By Sérgio Pereira, Raphael Meier, Victor Alves, Mauricio Reyes, Carlos Alberto Silva. 2018

82 citations recorded by the source corpus.

Glioblastoma Multiforme is a high grade, very aggressive, brain tumor, with patients having a poor prognosis. Lower grade gliomas are less aggressive, but they can evolve into higher grade tumors over time. Patient management and treatment can vary considerably with tumor grade, ranging from tumor resection followed by a combined radio- and chemotherapy to a “wait and see” approach. Hence, tumor grading is important for adequate treatment planning and monitoring. The gold standard for tumor grading relies on histopathological diagnosis of biopsy specimens. However, this procedure is invasive, time consuming, and prone to sampling error. Given these disadvantages, automatic tumor grading from widely used MRI protocols would be clinically important, as a way to expedite treatment planning and assessment of tumor evolution. In this paper, we propose to use Convolutional Neural Networks for predicting tumor grade directly from imaging data. In this way, we overcome the need for expert annotations of regions of interest. We evaluate two prediction approaches: from the whole brain, and from an automatically defined tumor region. Finally, we employ interpretability methodologies as a quality assurance stage to check if the method is using image regions indicative of tumor grade for classification.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Neurology

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Citations in the corpus

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019, methodology): “ist of direct applications of existing saliency methods. Chexpert [6] uses GradCAM for visualization of pleural effusion in a radiograph. CAM is also used for interpretability in brain tumour grading [152]. Reference [67] uses Guided Grad-CAM and feature occlusion, providing complementary heatmaps f”
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