Deep Learning in Medical Image Analysis
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Author: Dr. Hichem Felouat
Email: [email protected]
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1) Motivation, Objectives and Related Works:
Motivation:
Medical imaging is the technique and process of creating visual representations of the interior of a body for clinical analysis and medical intervention, as well as visual representation of the function of some organs or tissues.
Objectives:
Medical imaging seeks to reveal internal structures hidden by the skin and bones, as well as to diagnose and treat diseases.
Modalities:
Visualization:
Visualization is the process of exploring, transforming, and viewing data as images to gain understanding and insight into the data, which requires fast interactive speed and high image quality.
Library: nibabel
Read / write access to some common neuroimaging file formats
Anatomist:
2) Methods:
Image Regression for Medical Image Analysis
Brain age prediction using deep learning
Medical Image Captioning Using DL:
Medical Image Captioning Using Optimized Deep Learning Model
GANs for medical image analysis:
Vision Transformer (ViT) for Image Classification:
Graph Neural Network in Medical Image Analysis:
BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis
Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
Self-Supervised and Semi-Supervised Learning in Medical Image Analysis:
Uncertainty Guided Semi-supervised Segmentation of Retinal Layers in OCT Images
https://link.springer.com/chapter/10.1007/978-3-030-32239-7_32
Semi-Supervised Learning in Computer Vision
A Survey of Self-Supervised and Few-Shot Object Detection
3) Experimental Results:
Experimental Results:
Ablations:
References:
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