Publications by authors named "F Khalvati"

Magnetic Resonance Imaging (MRI) serves as a valuable tool for detecting abnormalities in brain structures. However, a notable 5-10% of pathologies remain unnoticed in MRI scans. To address this challenge and reduce the burden on radiologists, machine learning methods have been used to automate anomaly detection.

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Scoliosis is a three-dimensional deformity of the spine, often diagnosed in childhood. It affects 2-3% of the population, representing seven million people in North America. Currently, the gold standard for assessing scoliosis is done manually by measuring Cobb angles.

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Medical image analysis has significantly benefited from advancements in deep learning, particularly in the application of Generative Adversarial Networks (GANs) for generating realistic and diverse images that can augment training datasets. The common GAN-based approach is to generate entire image volumes, rather than the region of interest (ROI). Research on deep learning-based brain tumor classification using MRI has shown that it is easier to classify the tumor ROIs compared to the entire image volumes.

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Article Synopsis
  • This study focuses on creating a machine learning pipeline that can segment brain tumors in medical images using only binary image-level classification labels, eliminating the need for expensive and time-consuming manual annotations.
  • The method combines a deep superpixel generation model and a clustering model that work together to produce weakly supervised tumor segmentations while utilizing a classifier to improve accuracy by focusing on undersegmented areas.
  • The new pipeline was evaluated using MRI scans from the BraTS 2020 and BraTS 2023 datasets, achieving promising results in segmentation performance compared to existing state-of-the-art methods.
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Emergency neuroradiology provides rapid diagnostic decision-making and guidance for management for a wide range of acute conditions involving the brain, head and neck, and spine. This narrative review aims at providing an up-to-date discussion about the state of the art of applications of artificial intelligence in emergency neuroradiology, which have substantially expanded in depth and scope in the past few years. A detailed analysis of machine learning and deep learning algorithms in several tasks related to acute ischemic stroke involving various imaging modalities, including a description of existing commercial products, is provided.

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