597 citations · 1.3k across the 17 of their papers we have counts for
19 papers
IConE: Batch Independent Collapse Prevention for Self-Supervised Representation Learning
Konstantinos Almpanakis, Anna Kreshuk
Self-supervised learning (SSL) has revolutionized representation learning, with Joint-Embedding Architectures (JEAs) emerging as an effective approach for capturing semantic featur…
Tiling artifacts and trade-offs of feature normalization in the segmentation of large biological images
Elena Buglakova, Anwai Archit, Edoardo D'Imprima +3
Segmentation of very large images is a common problem in microscopy, medical imaging or remote sensing. The problem is usually addressed by sliding window inference, which can theo…
Unsupervised Source-Free Ranking of Biomedical Segmentation Models Under Distribution Shift
Joshua Talks, Kevin Marchesini, Luca Lumetti +2
Model reuse offers a solution to the challenges of segmentation in biomedical imaging, where high data annotation costs remain a major bottleneck for deep learning. However, althou…
How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities
Charlotte Bunne, Yusuf Roohani, Yanay Rosen +39
The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determ…
Enabling Global Image Data Sharing in the Life Sciences
Peter Bajcsy, Sreenivas Bhattiprolu, Katy Boerner +20
Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences. In this White Paper, we tak…
MIFA: Metadata, Incentives, Formats, and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis
Teresa Zulueta-Coarasa, Florian Jug, Aastha Mathur +24
Artificial Intelligence methods are powerful tools for biological image analysis and processing. High-quality annotated images are key to training and developing new methods, but a…