arXiv Machine Learning August 18, 2026 By Ziwen Liu, Martin Weigert

Unsupervised Learning of Cell Instances with Generative Routing Pyramids

Read the original on arXiv Machine Learning →

arXiv:2608. 16810v1 Announce Type: cross Abstract: Identifying and representing object instances such as cells or nuclei is a common task in microscopy image analysis.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

computer-vision

arXiv AI

Jul 7

HASSL: Hierarchy-Aware Self-Supervised Learning Framework for Single Cell Microscopy

arXiv:2607. 04353v1 Announce Type: cross Abstract: Hierarchical structure is common in image data, where fine-grained clusters often merge into larger, coarser semantic groups.

By Julius Riel, Vishwa Mohan Singh, Sai Anirudh Aryasomayajula, Anuun Chinbat, Hannes Leonhard, Moritz Ladenburger, Frederik Alexander, Vishisht Choudhary, Fabio Laredo, Giacomo Masserdotti, Thorben Prein, Carsten Marr, Amirhossein Kardoost

rag diffusion computer-vision efficiency

arXiv Machine Learning

1d ago

Deep Learning for BioImaging: What Are We Really Learning?

arXiv:2603. 13377v2 Announce Type: replace-cross Abstract: Representation learning has driven major advances in natural image analysis by enabling models to acquire high-level semantic features.

By Ivan Svatko, Maxime Sanchez, Ihab Bendidi, Gilles Cottrell, Auguste Genovesio

benchmarks

arXiv AI

Jul 28

scMIR: a vision-language foundation model for single-cell light microscopy image representation

arXiv:2607. 22712v1 Announce Type: cross Abstract: Single-cell light microscopy images have become an important data source for characterizing cell phenotypes, but their complexity and heterogeneity pose challenges to high-throughput automated analysis.

By Yifan Shang (Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China, College of Computer Science and Electronic Engineering, Hunan University, Changsha, China), Jiahui Tan (College of Computer Science and Electronic Engineering, Hunan University, Changsha, China), Xiangxiang Zeng (College of Computer Science and Electronic Engineering, Hunan University, Changsha, China), Renjie Zhou (Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China)

fine-tuning efficiency multimodal benchmarks safety

arXiv AI

Jun 17

SPATIA: Multimodal Generation and Prediction of Spatial Cell Phenotypes

arXiv:2507. 04704v3 Announce Type: replace-cross Abstract: Understanding how cellular morphology, gene expression, and spatial context jointly shape tissue function is a central challenge in biology.

By Zhenglun Kong, Mufan Qiu, John Boesen, Xiang Lin, Sukwon Yun, Tianlong Chen, Manolis Kellis, Marinka Zitnik

diffusion multimodal benchmarks safety

arXiv Machine Learning

Jul 17

A vision foundation model for single-cell biology via spatial gene cartography

arXiv:2607. 14163v1 Announce Type: cross Abstract: Most single-cell foundation models are adapted from language models, representing each cell as a sequence of gene tokens.

By Ridvan Yesiloglu, Sakib Mostafa, James Zou, Ash Alizadeh, Jiajun Wu, Lei Xing, Ehsan Adeli, Md Tauhidul Islam

llms computer-vision fine-tuning

Hugging Face Trending Papers

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Higher-Order Cell Tracking Transformer

Reconstructing lineages from live-imaging microscopy requires linking cell detections across time, including through cell divisions. A common approach is to construct a candidate graph and associate cell segmentations (nodes) across frames.

llms rag fine-tuning benchmarks

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HASSL: Hierarchy-Aware Self-Supervised Learning Framework for Single Cell Microscopy

Deep Learning for BioImaging: What Are We Really Learning?

scMIR: a vision-language foundation model for single-cell light microscopy image representation

SPATIA: Multimodal Generation and Prediction of Spatial Cell Phenotypes

A vision foundation model for single-cell biology via spatial gene cartography

Higher-Order Cell Tracking Transformer