Artificial Intelligence in Glioma Imaging: Challenges and Advances
arXiv:1911.12886 · doi:10.1088/1741-2552/ab8131
Abstract
Primary brain tumors including gliomas continue to pose significant management challenges to clinicians. While the presentation, the pathology, and the clinical course of these lesions are variable, the initial investigations are usually similar. Patients who are suspected to have a brain tumor will be assessed with computed tomography (CT) and magnetic resonance imaging (MRI). The imaging findings are used by neurosurgeons to determine the feasibility of surgical resection and plan such an undertaking. Imaging studies are also an indispensable tool in tracking tumor progression or its response to treatment. As these imaging studies are non-invasive, relatively cheap and accessible to patients, there have been many efforts over the past two decades to increase the amount of clinically-relevant information that can be extracted from brain imaging. Most recently, artificial intelligence (AI) techniques have been employed to segment and characterize brain tumors, as well as to detect progression or treatment-response. However, the clinical utility of such endeavours remains limited due to challenges in data collection and annotation, model training, and the reliability of AI-generated information. We provide a review of recent advances in addressing the above challenges. First, to overcome the challenge of data paucity, different image imputation and synthesis techniques along with annotation collection efforts are summarized. Next, various training strategies are presented to meet multiple desiderata, such as model performance, generalization ability, data privacy protection, and learning with sparse annotations. Finally, standardized performance evaluation and model interpretability methods have been reviewed. We believe that these technical approaches will facilitate the development of a fully-functional AI tool in the clinical care of patients with gliomas.
31 pages, 6 figures. Accepted for publication in the Journal of Neural Engineering
References in corpus (33)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Auto-Encoding Variational Bayes
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
- Deep Learning with Differential Privacy
- Brain Tumor Segmentation with Deep Neural Networks
- Towards A Rigorous Science of Interpretable Machine Learning
- Striving for Simplicity: The All Convolutional Net
- Neural Architecture Search: A Survey
- Confounding variables can degrade generalization performance of radiological deep learning models
- CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
- Understanding deep learning requires rethinking generalization
- What do we need to build explainable AI systems for the medical domain?
- Transfusion: Understanding Transfer Learning for Medical Imaging
- Wasserstein GAN
- fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
- Adversarial Autoencoders
- What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use
- A Survey on Neural Architecture Search
- Split learning for health: Distributed deep learning without sharing raw patient data
- Learning Active Learning from Data
- Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders
- Machine learning and glioma imaging biomarkers
- Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT
- Exploring Randomly Wired Neural Networks for Image Recognition
- Artificial Intelligence in Glioma Imaging: Challenges and Advances
- No Peek: A Survey of private distributed deep learning
- Medical Image Imputation from Image Collections
- Towards continual learning in medical imaging
- Learning under Distributed Weak Supervision
- Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs
- Towards continuous learning for glioma segmentation with elastic weight consolidation
- mustGAN: Multi-Stream Generative Adversarial Networks for MR Image Synthesis
- Which Contrast Does Matter? Towards a Deep Understanding of MR Contrast using Collaborative GAN
Cited by in corpus (8)
- Guidelines and Evaluation of Clinical Explainable AI in Medical Image Analysis
- Hierarchical Amortized Training for Memory-efficient High Resolution 3D GAN
- Evaluating Explainable AI on a Multi-Modal Medical Imaging Task: Can Existing Algorithms Fulfill Clinical Requirements?
- Attention-based Saliency Maps Improve Interpretability of Pneumothorax Classification
- Artificial Intelligence in Glioma Imaging: Challenges and Advances
- Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review
- One Map Does Not Fit All: Evaluating Saliency Map Explanation on Multi-Modal Medical Images
- AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics