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Overview

Medical images are obtained from various sources including MRI, CT, X-ray, ultrasound, and PET scans. Analyzing these images necessitates a comprehensive environment for data access, visualization, processing, and algorithm development. A key challenge involves extracting quantitative features to generate clinically relevant information using advanced techniques like machine learning algorithms.  This presentation will explore radiomics, which captures characteristics not typically visible. Radiomics features can be employed across various medical imaging modalities and applications, enabling the study of associations between imaging features and patient biology, as well as the prediction of clinical outcomes, making radiomics a versatile technique in medical imaging. These features quantify shape, intensity, and texture characteristics within medical images, reducing reliance on subjective interpretation for clinical workflows.

Highlights

In this session, you will learn how to:

• Import and manage large sets of images without loading them into memory,

• Extract shape, intensity, and texture radiomics features,

• Reduce the number of extracted features for classification,  

• Build a deep learning network to classify images, and

• Review and analyze the results.

In this workshop we invite users of the HMS Research Computing Cluster O2 and members of the Harvard community who are interested in using a compute cluster to join us as we demonstrate some very helpful tips and best practices. We will introduce participants to various commands and approaches to help effectively navigate use the cluster and complete tasks in an efficient manner. We encourage participants to log on to the cluster and follow along interactively, however attendees can also watch the demonstration. This workshop is being held in collaboration with HMS Research Computing, and is an advanced workshop requiring knowledge of the command-line and/or the basic shell skills learned in The Foundation - Basic Shell.