Abstract
How can machine learning enhance biomedical image analysis? This talk explores the potential of Python and PyTorch in automating artifact and damage segmentation. From data preprocessing to clustering-based label classification and deep learning-driven segmentation, key techniques will be discussed, including the use of Convolutional Neural Network architectures. The session will also cover performance evaluation and insights into advancing biomedical imaging with AI-driven solutions.
Description
The field of pathology is undergoing rapid digitalization, integrating new technologies that optimize workflows, reduce manual effort, and enhance diagnostic accuracy. However, preprocessing remains a labor-intensive step, requiring specialists to manually segment out artifacts before further analysis can take place. This talk explores how Python and PyTorch can be leveraged to automate this crucial preprocessing stage using machine learning techniques.
The session will begin with an overview of the challenges in biomedical image segmentation, emphasizing the importance of artifact detection in digital pathology. It will then explore machine learning-based approaches, particularly the role of convolutional neural networks (CNNs) in automating segmentation. Special focus will be given to U-Net and YOLO architectures, discussing their efficiency in handling biomedical image data. Additionally, the talk will cover label classification using clustering algorithms such as K-Means and DBSCAN, which help refine annotation quality and improve segmentation outcomes.
The discussion will extend to model training, evaluation, and the comparison of different architectures based on performance metrics. Practical insights from experimental results will be shared, including challenges encountered during development and potential solutions. The session will conclude by addressing the broader impact of AI-powered segmentation in digital pathology, discussing future advancements, ethical considerations, and recommendations for further research.
Attendees will gain a deeper understanding of how Python and PyTorch can streamline biomedical image preprocessing, reduce manual workload, and enhance the accuracy of pathological analysis through automation.
Keywords: Semantic segmentation, Computer Vision, Convolutional Neural Network, Clustering, Semi-supervised Learning, Digital Pathology, Machine Learning with Python
