07-08, 13:30–17:30 (US/Pacific), Ballroom A
Artificial intelligence has been successfully applied to bioimage understanding and achieved significative results in the last decade. Advances in imaging technologies have also allowed the acquisition of higher resolution images. That has increased not only the magnification at what images are captured, but the size of the acquired images as well. This comprises a challenge for deep learning inference in large-scale images, since these methods are commonly used in relatively small regions rather than whole images. This workshop presents techniques to scale-up inference of deep learning models to large-scale image data with help of Dask for parallelization in Python.
Methods such as U-Net, Cellpose, Stardist, and even adaptations of the Segment Anything Model for microscopy data, Micro-SAM, have been used extensively by the bioimage analysis community. These methods already offer efficient pipelines for their application in sub-regions of high-resolution images, such as Whole Slide Images (WSI). However, limitations in memory capacity of computer systems restrict their applicability to whole images, requiring manual extraction of image tiles for their individual analysis, and subsequent merging of the inference results.
On the other hand, advances in image data management and storage, such as Next Generation File Formats (Zarr), and libraries for parallel computation, such as Dask, enable scaling-up the existing pipelines now without these limitations on memory. In this workshop, techniques to scale-up machine learning inference to large-scale images without manual extraction of tiles will be explored.
This workshop is focused on applications in microscopy bioimage analysis, but the techniques learned during this workshop can be applied to any other modality. Finally, because the reviewed techniques use high-level functions from the Dask library, these can be executed on common laptops or High Performance Computing environments, depending on the scale of the analyzed images.
Basic understanding of Image analysis.
Preferable but not strictly necessary, basic understanding of machine learning inference.
PhD in Computer Science focused in bioimage understanding through computational intelligence methods.
I currently work as Systems Analyst in the Research IT department of the The Jackson Laboratory, where my main role is assisting people with integration of machine learning methods in their image analysis pipelines.
Peter is a napari core developer and a Systems Analyst in the Imaging Applications and Machine Learning team in Research IT at The Jackson Laboratory. In this day job, Peter supports users of open source imaging applications and workflows, both on local devices and on HPC. He also runs workshops to help users ease into the various applications. As a napari core developer, he focuses on user-facing bugs and issues, UI/UX, documentation, etc.