The paper introduces an end‑to‑end framework for detecting and classifying cells in pathology images by jointly modeling visual features and instance‑level interactions. It employs a dynamic graph construction module that builds cell graphs from learnable queries and an instance‑aware graph network that filters and reorganizes features, integrating appearance and relational evidence. Experiments on multiple staining protocols show the method surpasses existing approaches in both detection and classification accuracy.
By Ruochen Liu, Yalin Zheng, Jingxin Liu, Jianfeng Zhang, Shoujun Huang, Dexing Kong, Haofeng Li, Wei Lou
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
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.
By Ziwen Liu, Martin Weigert
The paper introduces scalable Graph Transformers for classifying healthy versus tumor epithelial cells in whole-slide images of cutaneous squamous cell carcinoma. By constructing a full‑WSI cell graph and incorporating morphological, texture, and neighboring cell class features, the proposed SGFormer and DIFFormer models outperform traditional image‑based methods, achieving balanced accuracies above 85% on single‑WSI tests and 83.6% on multi‑WSI evaluations. The study demonstrates that preserving tissue‑level context through graph representations improves classification of morphologically similar cell types.
By Lucas Sanc\'er\'e, No\'emie Moreau, Katarzyna Bozek
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
The paper presents a 3D foundation model for light sheet fluorescence microscopy (LSM) that is pretrained on a large curated set of 3D images from various organisms, stains, and imaging protocols. By jointly optimizing for masked reconstruction and image‑text alignment, the model learns transferable volumetric representations that dramatically reduce the need for annotated data. The pretrained backbone enables efficient few‑shot adaptation to downstream tasks such as segmentation, classification, and deblurring, consistently outperforming baselines according to standard metrics and expert evaluation.
By Adina Scheinfeld, Haotan Zhang, Shang Mu, Rudolf L. M. van Herten, Lucas Stoffl, Ali Erturk, Zhuhao Wu, Johannes C. Paetzold
arXiv:2606. 20437v1 Announce Type: cross Abstract: Charged-particle tracking -- reconstructing trajectories from sparse detector measurements -- is a fundamental high-energy-physics inference problem and a canonical example of learning under extreme combinatorial ambiguity.
By Siqi Miao, Shitij Govil, Jack P. Rodgers, Mia Liu, Javier Duarte, Shih-Chieh Hsu, Yuan-Tang Chou, Pan Li
arXiv:2606. 07633v1 Announce Type: cross Abstract: Accurate classification of nuclei subtypes in histopathology images is critical for downstream tasks including tumor grading, immune infiltrate quantification, and prognosis prediction.
By Spoorthi M, Suja Palaniswamy
arXiv:2609.36429v1 Announce Type: new
Abstract: Predicting gene expression from H&E-stained histology images offers a scalable alternative to costly spatial transcriptomics, yet most existing methods...
By Zijun Gao, Chunbin Gu, Jinxi Xiang, Xiangde Luo, Pheng-Ann Heng
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
arXiv:2609.16773v1 Announce Type: new
Abstract: Image segmentation remains challenging due to occlusions, poor lighting, and irregular structures. Although transformer-based methods achieve high accu...
By Ruibo Wang, Ziyi Shen, Huaming Wu, Dong Liang, Kun Shang
arXiv:2608.22281v1 Announce Type: cross
Abstract: Medical image segmentation requires high accuracy and robustness, yet practical commercial deployment also demands privacy preservation and computati...
By Bin Dong, Jinghong Chen