arXiv Computer Vision

MARC: Morphology-Aware Regression of Consensus for Cell Segmentation in Subcellular Spatial Transcriptomics

arXiv Computer Vision

4d ago

Towards Scalable Context-Aware Single-Cell Spatial Transcriptomics Prediction from Histology Images

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

computer-vision

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 AI

Jun 2

GC-MoE: Genomics-Guided Cell-Type-Specific Mixture of Experts for Histology-Based Single-Cell Spatial Transcriptomics

arXiv:2606. 02424v1 Announce Type: cross Abstract: Histology-based single-cell spatial transcriptomics (ST) estimation aims to predict gene expression for individual cells from histopathological images and cell locations, reducing the need for costly single-cell ST measurements.

By Kaito Shiku, Ahtisham Fazeel Abbasi, Ryoma Bise, Yuichiro Iwashita, Kazuya Nishimura, Andreas Dengel, Muhammad Nabeel Asim

arXiv AI

Aug 18

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

Jun 17

SegTME-UNI2: A Foundation Model-Based Framework for Generalisable Multiclass Cell Segmentation and LLM-Driven Tumour Microenvironment Characterisation in Histopathology

arXiv:2606. 17702v1 Announce Type: cross Abstract: Characterising the tumour microenvironment (TME) from routine H&E-stained histology images requires simultaneous cell segmentation, feature extraction, and interpretable clinical reporting.

By Wan Siti Halimatul Munirah Wan Ahmad, Faris Syahmi Samidi, Mohammad Badal Ahmmed, Vimal Angela Thiviyanathan, Selvam James Thavaraj, Anwar P. P. Abdul Majeed

llms computer-vision fine-tuning

arXiv Machine Learning

Jul 17

LATTICE: Graph Self-Supervised Learning for Multimodal Spatial Omics Integration

arXiv:2607. 14410v1 Announce Type: new Abstract: Spatially resolved omics studies increasingly combine transcriptomic and epigenomic assays, yet downstream analysis is often still performed using single-modality pipelines.

By Jagan Mohan Reddy Dwarampudi, Veena Kochat, Suresh Satpati, Kunal Rai, Tania Banerjee

rag multimodal benchmarks safety

arXiv AI

Aug 10

CellWorld: From Gene-Level Reconstruction to Latent Cell Prediction in Spatial Transcriptomics Foundation Models

arXiv:2608. 06659v1 Announce Type: new Abstract: This paper shows that latent-space predictive pretraining can provide a scalable route to foundation models for spatial transcriptomics.

By Haiping Liu, Qian Zhao, Lijing Lin, Jingyuan Sun, Hongpeng Zhou

fine-tuning benchmarks

arXiv Machine Learning

Jun 3

Spatial Transcriptomics-Guided Alignment Enhances Molecular Profiling in Pathology Foundation Model

arXiv:2606. 03644v1 Announce Type: new Abstract: Comprehensive molecular profiling is essential for modern precision oncology but remains hindered by prohibitive costs, specimen exhaustion, and protracted turnaround times.

By Fengtao Zhou, Yingxue Xu, Zhengyu Zhang, Yihui Wang, Zhengrui Guo, Ling Liang, Jiabo Ma, Cheng Jin, Ziyi Liu, Huajun Zhou, Hongyi Wang, Du Cai, Chenglong Zhao, Xi Wang, Can Yang, Yu Wang, Wenbin Li, Feng Gao, Zhe Wang, Zhenhui Li, Xiuming Zhang, Li Liang, Hao Chen

fine-tuning multimodal safety

arXiv Machine Learning

Jun 5

HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data

arXiv:2506. 11152v4 Announce Type: replace-cross Abstract: Single-cell transcriptomics and proteomics have become a great source for data-driven insights into biology, enabling the use of advanced deep learning methods to understand cellular heterogeneity and gene expression at the single-cell level.

By Hiren Madhu, Jo\~ao Felipe Rocha, Tinglin Huang, Siddharth Viswanath, Smita Krishnaswamy, Rex Ying

llms rag benchmarks

arXiv AI

Aug 17

Program-space Diffusion for Morphology-to-Transcriptomics Prediction

arXiv:2608. 14330v1 Announce Type: new Abstract: Spatial transcriptomics (ST) enables genome-wide gene expression profiling while preserving tissue architecture, but its cost and limited scalability remain major bottlenecks.

By Ruyter Swann, Dorent Reuben, Racoceanu Daniel

diffusion

arXiv Computer Vision

Sep 10

LSP-DETR: Efficient and Scalable Nuclei Segmentation in Whole-Slide Images

arXiv:2601.03163v2 Announce Type: replace Abstract: Background and Objective: Precise and scalable instance segmentation of cell nuclei is a fundamental prerequisite for computational pathology, yet...

By Mat\v{e}j Pek\'ar, V\'it Musil, Rudolf Nenutil, Petr Holub, Tom\'a\v{s} Br\'azdil

llms computer-vision efficiency benchmarks

arXiv Machine Learning

Jul 24

HierarchicalDAEW: Domain-Aware Edge-Weighted Graph Convolution with Evidential Uncertainty for Multi-Section Spatial Gene Expression Prediction from H&E Histology

arXiv:2607. 20896v1 Announce Type: new Abstract: Spatial transcriptomics assays remain costly and technically demanding, restricting transcriptome-wide profiling to specialist settings and preventing routine clinical deployment.

By Kritanu Chattopadhyay, Soumya Chatterjee, Ondrej Krejcar, Debotosh Bhattacharjee

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