arXiv Machine Learning

Unsupervised Learning of Cell Instances with Generative Routing Pyramids

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.

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

2d 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

Jul 13

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

arXiv Machine Learning

Jun 24

3D Masked Autoencoders are Robust Learners of Volumetric and Multimodal Cellular Representations for Microscopy

arXiv:2606. 23964v1 Announce Type: new Abstract: Self-supervised learning in fluorescence microscopy often relies on 2D projections, despite the inherently three-dimensional nature of cells.

By Amirhossein Kardoost, Lion Gleiter, Tingying Peng, Carsten Marr

llms multimodal benchmarks safety

arXiv AI

2d ago

Path2ST: Hierarchical Cell-Tissue Grounded Cross-Modal Translation for Spatial Transcriptomics

arXiv:2608. 14710v1 Announce Type: cross Abstract: Predicting spatial gene expression from hematoxylin and eosin (H\&E)-stained images offers a cost-effective alternative to spatial transcriptomics (ST).

By Ruochen Liu, Wei Lou

multimodal benchmarks

arXiv AI

Jul 14

Next-Dense-Stride Prediction for Multimodal Autoregressive Visual Modeling

arXiv:2607. 09892v1 Announce Type: cross Abstract: We introduce DenseAR, a new generative paradigm that reformulates autoregressive image generation as coarse-to-fine next-dense-stride prediction using a compact single-scale tokenizer.

By Chicago Y. Park, Jialin Mao, Xiaojian Xu, Taha Kass-Hout, Ulugbek S. Kamilov, Cao Xiao

diffusion computer-vision nlp multimodal

arXiv AI

Jun 8

DualGate-Net: A Prior-Gated Dual-Encoder Framework for Histopathology Cell Detection

arXiv:2606. 07222v1 Announce Type: cross Abstract: Cell detection in histopathology images strongly depends on surrounding tissue context, where visually similar cells may belong to different classes under different microenvironments.

By Bahman Jafari Tabaghsar, Son Tran, K. Devaraja, Atul Sajjanhar

benchmarks

arXiv AI

Jun 2

{\lambda}Split: Self-Supervised Content-Aware Spectral Unmixing for Fluorescence Microscopy

arXiv:2603. 23647v2 Announce Type: replace-cross Abstract: In fluorescence microscopy, spectral unmixing aims to recover individual fluorophore concentrations from spectral images that capture mixed fluorophore emissions.

By Federico Carrara, Talley Lambert, Mehdi Seifi, Florian Jug

benchmarks

arXiv Machine Learning

Jul 23

IConE: Batch Independent Collapse Prevention for Self-Supervised Representation Learning

arXiv:2603. 15263v2 Announce Type: replace-cross Abstract: Self-supervised learning (SSL) has revolutionized representation learning, with Joint-Embedding Architectures (JEAs) emerging as an effective approach for capturing semantic features.

By Konstantinos Almpanakis, Anna Kreshuk

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