A.I.B.M. - Interpretable AI-Powered System for Identification of Bone Metastases in Computed Tomography and Magnetic Resonance Imaging
Aim
Description
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 Latvia, aiming to optimize the model’s performance for local populations and contribute to the broader application of DL in medical diagnostics.
The project is divided into three phases:
- The initial task is to improve medical understanding of bone metastasis detection while exploring the most effective deep convolutional neural network (DCNN) structures and algorithms.
- The second task focuses on dataset segmentation while conducting research and experiments using established DCNN and visual transformer deep neural network (DNN) architectures and algorithms. The initial model, capable of segmenting pelvic bones and spinal vertebrae, will be developed through deep neural network training.
- Validation of the deep neural network model in daily clinical practice, compared to the assessment of an experienced radiologist.
Project Research Team
- Viktorija Cīrule
- Matīss Šņukuts
- Laura Saule
- Madara Ratniece
- Jana Solska
Project Collaboration Partner
