Brain tumor classification on the patient level using attention-based AI methods and multi-sequences MRI

Vladimir Groza, Bair Tuchinov Nikolaevich, Evgeniya Amelina, Evgeniy Nikolaevich Pavlovskiy, Nikolai Tolstokulakov, Mikhail Amelin, Sergey Golushko, Andrey Letyagin

Keywords: Brain tumor, Deep learning, Medical imaging, Classification, MRI, Clinical analysis

TL;DR: Brain tumor MRI classification

Abstract:

Investigation of brain tumor structure and its type-dependent variations are among the list of most important research directions where the medical imaging methods are used. Structural and statistical analysis of these lesions originates various associated problems and projects such as detection of the tumors, shape and specific sub-regions segmentation (i.e. necrotic part, (non-)enhanced part, edema), classification of the tumor presence and treatment follow up prognosis. Almost all of these problems are usually solved numerically, specifically with the tendency to use the Artificial Intelligence (AI) related methods often including Deep Learning (DL) networks.

One of the most complicated, weakly explored and challenging tasks in this domain is the classification of the tumor types. This difficulty is explained by several reason where the most principle one is the strong limitation of the existing open-sourced datasets that include clinically confirmed tumor type labels based on the radiological examination protocols.

In this work we present current results of the brain tumor classification problem, where we consider and operate with four different lesion types such as meningioma, neurinoma, glioblastoma and astrocytoma. All the conducted research and presented results are obtained on the newly introduced dataset including 255 labeled volume MRI scans describing wide variety of the tumors and its clinically associated ground truth (GT) information.

Obtained in this work results demonstrate not only inspiring and strong Accuracy performance of 0.925 on patient level (and accordingly 0.894 slice-wise) but also very high potential and perspective for the future research in this field.

Registration: I acknowledge that publication of this at MIDL and in the proceedings requires at least one of the authors to register and present the work during the conference.

Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.

Paper Type: validation/application paper

Primary Subject Area: Detection and Diagnosis

Secondary Subject Area: Application: Radiology

Confidentiality And Author Instructions: I read the call for papers and author instructions. I acknowledge that exceeding the page limit and/or altering the latex template can result in desk rejection.