Microscopy Image Analysis

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13 papers in the last four weeks, up 117% on the four weeks before. 0.1% of all new papers.

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Self-Supervised ImageNet Representations for In Vivo Confocal Microscopy: Tortuosity Grading without Segmentation Maps
Mar 16, 2026Kim Ouan, Noémie Moreau, Katarzyna BozekMedical Image Classification Microscopy Image Analysis
Faculty of Mathematics and Natural Sciences, University of Cologne, Germany · Institute for Biomedical Informatics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany · Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany +1
AFFMAE: Scalable Vision Pre-Training for High-Resolution Microscopy Segmentation on Desktop Hardware
Feb 18, 2026David Smerkous, Zian Wang, Behzad NajafianMasked Autoencoders Self-Supervised Pre-Training
Department of Laboratory Medicine & Pathology, University of Washington, Seattle WA, USA · Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR, USA · Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle WA, USA
EM3M: An Electron Micrograph Dataset for Microstructural Segmentation and Generation
Aug 22, 2025Nan Wang, Zhiyi Xia, Yiming Li +7Synthetic Data Augmentation Instance Segmentation
DP Technology, Beijing, China
AdaptiveCDM: Source-Free Few-Shot Domain Adaptation for Cell Detection in Microscopic Images
Mar 5, 2025Nimra Dilawar, Sara Nadeem, Javed Iqbal +2Source-Free Domain Adaptation Few-Shot Domain Adaptation
Intelligent Machines Lab, Department of Artificial Intelligence, Information Technology University, Pakistan
Segment Anything for Dendrites from Electron Microscopy
Nov 4, 2024Zewen Zhuo, Ilya Belevich, Ville Leinonen +4Image Segmentation Vision Foundation Models
A.I. Virtanen Institute for Molecular Sciences University of Eastern Finland Kuopio, Finland · Electron Microscopy Unit Institute of Biotechnology University of Helsinki Helsinki, Finland · Department of Medicine Faculty of Health Sciences University of Eastern Finland Kuopio, Finland

Mar 16, 2026, cs.CVJ/K move · Enter open · S save

Self-Supervised ImageNet Representations for In Vivo Confocal Microscopy: Tortuosity Grading without Segmentation Maps

Kim Ouan, Noémie Moreau, Katarzyna Bozek

Faculty of Mathematics and Natural Sciences, University of Cologne, Germany · Institute for Biomedical Informatics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany · Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany+1

Medical Image Classification Microscopy Image Analysis

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The tortuosity of corneal nerve fibers are used as indication for different diseases. Current state-of-the-art methods for grading the tortuosity heavily rely on expensive segmentation maps of these nerve fibers. In this paper, we demonstrate that self-supervised pretrained features from ImageNet are transferable to the domain of in vivo confocal microscopy. We show that DINO should not be disregarded as a deep learning model for medical imaging, although it was superseded by two later versions. After careful fine-tuning, DINO improves upon the state-of-the-art in terms of accuracy (84,25%) and sensitivity (77,97%). Our fine-tuned model focuses on the key morphological elements in grading without the use of segmentation maps.

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Latest papers 125

Self-Supervised ImageNet Representations for In Vivo Confocal Microscopy: Tortuosity Grading without Segmentation Maps

AFFMAE: Scalable Vision Pre-Training for High-Resolution Microscopy Segmentation on Desktop Hardware

EM3M: An Electron Micrograph Dataset for Microstructural Segmentation and Generation

AdaptiveCDM: Source-Free Few-Shot Domain Adaptation for Cell Detection in Microscopic Images

Segment Anything for Dendrites from Electron Microscopy