Abstract
In recent years, technological advances in microscopy have made available large amounts of data to biomedical researchers in the form of images. By learning from such large datasets, deep learning-based methods have successfully addressed previously inaccessible bioimage analysis tasks. However, most available solutions target a particular subset of problems, forcing users to be familiarized with different applications to complete their data analysis. On top of that, other issues, such as reproducibility, lack of documentation, or access to the code, arise. For these reasons, we introduce BiaPy, an open-source ready-to-use all-in-one library that provides deep-learning workflows for a large variety of bioimage analysis tasks, including 2D and 3D semantic and instance segmentation, object detection, super-resolution, denoising, self-supervised learning, and classification. All code and documentation are publicly available at https://github.com/danifranco/BiaPy.
Daniel Franco-Barranco
Postdoctoral Researcher
My primary focus is on the development of deep learning solutions for the segmentation of organelles in large-scale and multimodal electron microscopy images.
Ignacio Arganda-Carreras
Ikerbasque Research Associate Professor
My research interests include image processing, computer vision, and deep learning for biomedical applications.