4 citations · 4 across the 9 of their papers we have counts for
9 papers
Match: Foundation Models for Semi-supervised Learning and Domain Adaptation in EM
Marei Freitag, Olesia Korchevaia, Luca Freckmann +2
Vision foundation models have substantially advanced computer vision, enabling state-of-the-art performance in zero- and few-shot settings. They have been successfully applied to b…
CellNet -- Localizing Cells using Sparse and Noisy Point Annotations
Benjamin Eckhardt, Dmytro Fishman, Stuart Fawke +3
Counting living cells is an important step in many biological research workflows. Our collaborators at the Wellcome Sanger Institute study vital genes in humans via large scale sat…
Revisiting foundation models for cell instance segmentation
Anwai Archit, Constantin Pape
Cell segmentation is a fundamental task in microscopy image analysis. Several foundation models for cell segmentation have been introduced, virtually all of them are extensions of…
Evaluating Vision Foundation Models for Pixel and Object Classification in Microscopy
Carolin Teuber, Anwai Archit, Tobias Boothe +3
Deep learning underlies most modern approaches and tools in computer vision, including biomedical imaging. However, for interactive semantic segmentation (often called pixel classi…
BioimageAIpub: a toolbox for AI-ready bioimaging data publishing
Stefan Dvoretskii, Anwai Archit, Constantin Pape +2
Modern bioimage analysis approaches are data hungry, making it necessary for researchers to scavenge data beyond those collected within their (bio)imaging facilities. In addition t…
Parameter Efficient Fine-Tuning of Segment Anything Model for Biomedical Imaging
Carolin Teuber, Anwai Archit, Constantin Pape
Segmentation is an important analysis task for biomedical images, enabling the study of individual organelles, cells or organs. Deep learning has massively improved segmentation me…
Segment Anything for Histopathology
Titus Griebel, Anwai Archit, Constantin Pape
Nucleus segmentation is an important analysis task in digital pathology. However, methods for automatic segmentation often struggle with new data from a different distribution, req…
MedicoSAM: Robust Improvement of SAM for Medical Imaging
Anwai Archit, Luca Freckmann, Constantin Pape
Medical image segmentation is an important analysis task in clinical practice and research. Deep learning has massively advanced the field, but current approaches are mostly based…
ViM-UNet: Vision Mamba for Biomedical Segmentation
Anwai Archit, Constantin Pape
CNNs, most notably the UNet, are the default architecture for biomedical segmentation. Transformer-based approaches, such as UNETR, have been proposed to replace them, benefiting f…