Lectures related to Medical image computing

Explores drone imaging analysis using Meshroom software for civil engineering applications.

Covers the basics of machine learning for physicists and chemists, focusing on image classification and dataset labeling.

Explores soil sealing impact, land use statistics, image segmentation, and random forest classification for sustainable land management.

Covers the theory and algorithms behind image segmentation, focusing on region identification and evaluation.

Covers image registration and transformation using MultiStackReg plugin in Fiji.

Explores image processing in 2D and 3D, covering ideal imaging conditions, histogram analysis, tools, 3D reconstruction steps, and visualization.

Explores composition, pooling, continuity, denoising, and popular CNN architectures for image segmentation in image processing.

Explores segmentation techniques in image analysis, including thresholding, clustering, region growing, and machine learning.

Explores linear applications in 3D, emphasizing images, kernels, and solution uniqueness in systems.

Explores the challenges and solutions for managing electron dose in microscopy, emphasizing the importance of accurate dose tracking and analysis.

Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.

Covers the ABBA workflow for processing and analyzing brain images using QuPath and OMERO.

Covers fostering advanced imaging through interdisciplinary collaborations and high-impact developments at EPFL.

Covers setting measurements, managing results, using macro language functions, getting statistics, and saving results as CSV files.

Covers regions of interest, unbiased experiments, results storage, and batch image analysis.

Explores the significance of ROIs in measurements and how to manipulate them.

Covers the process of analyzing particles in images using Fiji software.

Covers the use of a DJI Mavic Pro drone for quantitative imaging in civil engineering, including image acquisition and processing techniques.

Explores image classification using decision trees and random forests to reduce variance and improve model robustness.

Discusses analyzing intensity and localization in images with fluorescent channels for protein expression changes.