4 citations · 8 across the 7 of their papers we have counts for
7 papers
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…
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…
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…
Probabilistic Domain Adaptation for Biomedical Image Segmentation
Anwai Archit, Constantin Pape
Segmentation is a crucial analysis task in biomedical imaging. Given the diverse experimental settings in this field, the lack of generalization limits the use of deep learning in…