Robust Tumor Localization with Pyramid Grad-CAM

TL;DR

This paper introduces PG-CAM, a novel weakly supervised tumor localization method using a multi-scale Grad-CAM approach with a feature pyramid network, demonstrating improved accuracy in meningioma MRI detection.

Contribution

The paper presents PG-CAM, a new pyramid gradient-based class activation mapping technique that enhances tumor localization accuracy with weak supervision in medical imaging.

Findings

PG-CAM outperforms Grad-CAM by 23% in localization accuracy.

The method effectively captures hierarchical tumor features.

Validated on meningioma MRI data from a hospital.

Abstract

A meningioma is a type of brain tumor that requires tumor volume size follow ups in order to reach appropriate clinical decisions. A fully automated tool for meningioma detection is necessary for reliable and consistent tumor surveillance. There have been various studies concerning automated lesion detection. Studies on the application of convolutional neural network (CNN)-based methods, which have achieved a state-of-the-art level of performance in various computer vision tasks, have been carried out. However, the applicable diseases are limited, owing to a lack of strongly annotated data being present in medical image analysis. In order to resolve the above issue we propose pyramid gradient-based class activation mapping (PG-CAM) which is a novel method for tumor localization that can be trained in weakly supervised manner. PG-CAM uses a densely connected encoder-decoder-based feature pyramid network (DC-FPN) as a backbone structure, and extracts a multi-scale Grad-CAM that captures hierarchical features of a tumor. We tested our model using meningioma brain magnetic resonance (MR) data collected from the collaborating hospital. In our experiments, PG-CAM outperformed Grad-CAM by delivering a 23 percent higher localization accuracy for the validation set.