Anomaly Detection in MRI Scans through Deep Learning: A Healthy Cohort Training Approach

Description:

The early detection of neurological abnormalities through Magnetic Resonance Imaging (MRI) is crucial in the medical field, potentially leading to timely interventions and better patient outcomes. However, the traditional diagnostic process is often time-consuming and subject to human error. This project seeks to improve this aspect by employing deep learning for anomaly detection in MRI scans, exclusively using images from healthy participants for model training [1].

The project aims to develop the algorithm to understand the parameters of a 'healthy' MRI scan [2], subsequently identifying deviations indicative of potential anomalies or pathological changes. This strategy significantly minimizes the bias introduced by various disease-specific patterns, enabling the model to identify unknown or unexpected anomalies efficiently.

The goal is to develop a robust, sensitive, and unbiased anomaly detection system that can assist healthcare professionals in early diagnosis and treatment planning, thereby improving patient care and management.

What will students learn in this project:

Participants in this project will submerge themselves in various learning opportunities:

• Deep Learning Techniques: Understand the nuances of deep learning algorithms and their application in anomaly detection, specifically within the realm of medical imaging.
• MRI Imaging Insights: Gain deep insights into MRI technology, focusing on the characteristics of scans from healthy individuals, understanding the subtle differences that indicate potential issues.
• Data Management and Ethics: Learn the principles of managing sensitive health data, ensuring the highest standards of privacy, confidentiality, and ethical research practices.
• Algorithm Training: Acquire hands-on experience in training deep learning models, understanding the importance of data, and nuances of 'healthy' baselines in training phases.
• Validation and Testing: Engage in the validation and testing of trained models, using unseen data to assess algorithm performance, and understand the practical challenges of real-world application.
• Research Documentation: Develop skills in documenting research findings, preparing reports, and contributing to scientific literature.
• Interdisciplinary Collaboration: Work in an interdisciplinary environment, collaborating with professionals in healthcare, machine learning, and data science.