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Anwai Archit

University of Göttingen

4 papers hereh-index 6306 citations11 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author3

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV2
  • eess.IV2
affiliations
  • University of Göttingen
Homepage
same name
  • Anwai Archit — 2 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedTiling artifacts and trade-offs of feature normalization in the segmentation of large biological images

1 citations · 1 across the 1 of their papers we have counts for

collaborators

4 papers

cs.CV2025★ 1 cited

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…

cs.CV2025

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…

eess.IV2025

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…

eess.IV2025

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.