A.I.B.M. - Interpretable AI-Powered System for Identification of Bone Metastases in Computed Tomography and Magnetic Resonance Imaging

The proposed research aims to utilize the capabilities of Deep Learning (DL) to improve the accuracy of medical diagnostic imaging, specifically in the detection of metastases within computed tomography (CT) and magnetic resonance imaging (MRI). This study will employ 3D fully convolutional neural networks to analyze complex data patterns from medical imaging, focusing on identifying subtle anatomical structures associated with metastases in bone tissues. The project will develop a Deep Neural Network (DNN) with a modular architecture, incorporating convolutional layers and a diffusion denoising probability method to enhance the detection and characterization of dispersed metastatic lesions. These lesions present considerable detection challenges due to their size and variability. The network will be trained, validated, and tested on an annotated radiology dataset from Latvian hospitals, aiming to optimize the model’s performance for local populations and contribute to the broader application of DL in medical diagnostics.