Projects

Data-driven approach for brain tumor segmentation

• Description: This work aims to propose a novel lightweight model to deal with brain tumor segmentation while decreasing the computational costs.
• Code: GitHub repo
• Paper: BTS U-Net: A data-driven approach to brain tumor segmentation through deep learning
• Main contributions:
  • We propose a novel efficient lightweight architecture that outperforms some of the most popular architectures previously used in biomedical image segmentation, such as U-Net or V-Net. Our model offers significantly lower training and computational requirements while achieving comparable performance.
  • The study shows statistical differences between LGG and HGG, and suggests a potential shift in glioma segmentation strategies to optimize outcomes, especially for HGG tumors, the most aggressive ones.
  • We prove that the brain tumor segmentation problem can be effectively approached in a 2-step way by first classifying the type of glioma between HGG and LGG, and then segmenting the MRI.

Multi-task learning: breast cancer classification and segmentation

• Description: This project presents a novel multi-task framework designed to enhance the accuracy and efficiency of breast cancer diagnosis using ultrasound imaging.
• Code: GitHub repo
• Paper: A multi-task framework for breast cancer segmentation and classification in ultrasound imaging
• Main contributions:
  • The multi-task framework significantly outperforms single-task approaches in terms of both segmentation and classification of breast cancer lesions.
  • Comprehensive analysis and curation of the BUSI dataset ensure minimized biases and more reliable outcomes.
  • The methodology showcases better generalization capabilities, crucial for clinical applications in breast cancer detection.

AUDIT: Analysis and evaluation dashboard of Artificial Intelligence models

• Description: This project presents a novel python library designed to evaluate AI segmentation models and MRI datasets.
• Code: GitHub repo
• Paper: AUDIT: An open-source Python library for AI model evaluation with use cases in MRI brain tumor segmentation
• Main contributions:
  • Open-source Python library for evaluating medical image segmentation models and analyzing MRI datasets.
  • Modular design inspired by modern deep learning frameworks to support integration with external libraries.
  • Extracts multiple features, including statistical, textural, spatial, and tumor-specific characteristics.
  • Includes an interactive web application for dynamic data exploration and visualization.
  • Supports statistical testing, longitudinal analysis, and subject-level bias analysis.