Multiple Instance Learning for Brain Tumor Detection from Magnetic Resonance Spectroscopy Data

12/16/2021
by   Diyuan Lu, et al.
0

We apply deep learning (DL) on Magnetic resonance spectroscopy (MRS) data for the task of brain tumor detection. Medical applications often suffer from data scarcity and corruption by noise. Both of these problems are prominent in our data set. Furthermore, a varying number of spectra are available for the different patients. We address these issues by considering the task as a multiple instance learning (MIL) problem. Specifically, we aggregate multiple spectra from the same patient into a "bag" for classification and apply data augmentation techniques. To achieve the permutation invariance during the process of bagging, we proposed two approaches: (1) to apply min-, max-, and average-pooling on the features of all samples in one bag and (2) to apply an attention mechanism. We tested these two approaches on multiple neural network architectures. We demonstrate that classification performance is significantly improved when training on multiple instances rather than single spectra. We propose a simple oversampling data augmentation method and show that it could further improve the performance. Finally, we demonstrate that our proposed model outperforms manual classification by neuroradiologists according to most performance metrics.

READ FULL TEXT
research
11/25/2021

Non Parametric Data Augmentations Improve Deep-Learning based Brain Tumor Segmentation

Automatic brain tumor segmentation from Magnetic Resonance Imaging (MRI)...
research
06/17/2020

Human-Expert-Level Brain Tumor Detection Using Deep Learning with Data Distillation and Augmentation

The application of Deep Learning (DL) for medical diagnosis is often ham...
research
08/28/2022

Detection and Classification of Brain tumors Using Deep Convolutional Neural Networks

Abnormal development of tissues in the body as a result of swelling and ...
research
08/23/2018

Generating Magnetic Resonance Spectroscopy Imaging Data of Brain Tumours from Linear, Non-Linear and Deep Learning Models

Magnetic Resonance Spectroscopy (MRS) provides valuable information to h...
research
07/30/2023

Mask-guided Data Augmentation for Multiparametric MRI Generation with a Rare Hepatocellular Carcinoma

Data augmentation is classically used to improve the overall performance...
research
10/17/2019

Anatomically-Informed Data Augmentation for functional MRI with Applications to Deep Learning

The application of deep learning to build accurate predictive models fro...
research
06/27/2018

MTBI Identification From Diffusion MR Images Using Bag of Adversarial Visual Features

In this work, we propose bag of adversarial features (BAF) for identifyi...

Please sign up or login with your details

Forgot password? Click here to reset