This project explores the potential of transformer-based deep learning models to revolutionize colorectal gland segmentation in medical imaging. By leveraging advanced architectures and attention mechanisms, the study aims to enhance the precision and efficiency of diagnosing colorectal cancer, a critical step in personalized treatment planning.
Key Highlights:
Transformative Approach: Evaluated state-of-the-art architectures, including UNet, DeepLabv3 with ResNet-101, SwinUNETR, SegFormer, and UNETR, to benchmark their performance in segmenting colorectal glands.
Advanced Dataset Utilization: Used the GlandVision dataset for training, with over 30,000 histopathological images, and validated the models using two widely recognized datasets, GLaS and CRAG, ensuring comprehensive evaluation.
Class Imbalance Solutions: Tackled challenges like severe class imbalance and inconsistent image sizes by implementing advanced preprocessing techniques and optimizing loss functions like Focal Tversky and Lovasz-Softmax.
Performance Insights: Achieved the highest segmentation accuracy with traditional CNN architectures like UNet and DeepLabv3, while also demonstrating competitive results with transformer-based models, highlighting their promise for future applications.
Innovative Results: Demonstrated that attention-based transformers like SwinUNETR effectively capture complex glandular structures, underscoring their potential to handle intricate segmentation tasks in medical imaging.
Why It Matters:
This project represents a significant step forward in the integration of cutting-edge machine learning techniques into healthcare. By advancing the capabilities of colorectal gland segmentation, it supports earlier and more accurate cancer diagnoses, paving the way for improved patient outcomes and more personalized treatments.