Learning to leverage salient regions in neuro-oncology using Deep Learning

Developing an automatic tumor detector for MRI medical images is a major challenge in neuro-oncology. The availability of such a tool would be a valuable assistance for the radiologists. Numerous works have tried to segment the tumor tissues, others have attempted to localize the tumor globally. In this work we focus on this second class of methods and we compare two drastically different strategies. The first one is an assumption-free anomaly detector build over a Variational Auto-Encoder (VAE), and the second one is a VGG classifier that embed Attention-Gated (AG) units to focus on the target structures at almost no additional computational cost. This comparison is first conducted on the publicly available BraTS glioma dataset for which published performance results can serve as reference, and extended as such (ie., without transfer learning) to two internal image datasets, namely Primary Central Nervous System Lymphoma (PCNSL) and Metastasis. The results demonstrate that the VAE and AG-VGG strategies can be used, up to a certain extent, to localize brain tumors.