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Zhetao Xu

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2 papers

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AAAI Conference 2026 Conference Paper

Cyto-SSL: A Self-Supervised Pretraining Framework for Cytology Foundation Model

Yiming Zhang
Rui Yan
Xiaohua Wan
Yifan Zhao
Shuang Feng
Zhetao Xu
Ying Wang
Fa Zhang

Cytological images originate from exfoliated cells, collected via liquid-based slides and digitized into whole slide images (WSIs). Unlike histological WSIs that exhibit continuous and well-structured tissue, cytological WSIs are sparse in spatial distribution and unstructured in cellular relationships. Typically, the nucleus serves as the primary diagnostic feature, while surrounding cytoplasmic information plays a supportive role. These unique characteristics limit the development of effective foundation models and hinder the transferability of histology-based models for cytopathology. To address this, we propose **Cyto-SSL**, the first self-supervised pretraining framework for cytological images. It introduces **Nuclei-Centered Perturbation**, which highlights individual nuclei by perturbing non-nuclear regions. We also design an SR-Transformer module, which complements this by using sparse attention to concentrate on diagnostically relevant scattered cells, while iRPE helps model to capture local spatial relationships and avoids unnecessary attention to irrelevant global structures. Experimental results show that **Cyto-SSL** enhances performance across diverse cytological datasets and Multiple Instance Learning (MIL) methods. On a WSI-level dataset, it achieved 95.67% accuracy and outperformed ImageNet-pretrained ResNet-50 by 11.33%, demonstrating superior feature representation for cytological analysis. Additionally, **Cyto-SSL** modules are plug-and-play, easily integrated into other pretraining frameworks, yielding a 2.6% accuracy gain across different SSL methods.

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AAAI Conference 2026 Conference Paper

ST-LLM: Spatial Transcriptomics Embedding with Large Language Models

Zhetao Xu
Xiaohua Wan
Le Li
Shuang Feng
Yiming Zhang
Fa Zhang
Bin Hu

Spatial transcriptomics provides unprecedented opportunities to analyze gene patterns while preserving spatial tissue architecture. However, traditional deep learning methods for spatial transcriptomics analysis face significant challenges in multi-modal data integration, spatial dependency modeling, and biological knowledge incorporation, while existing large language models lack explicit spatial modeling capabilities for transcriptomic data. So we first present a Spatial Transcriptomics Embedding with Large Language Models (ST-LLM), a novel simple and effective approach that transforms intricate spatial graph structures into structured textual representations suitable for large language models (LLMs). ST-LLM dynamically constructs graph adjacency construction using reinforcement learning paradigms to adaptively optimize spatial relationships, converts the resulting graphs into hierarchical textual descriptions with spatial context, and leverages pre-trained semantic understanding to generate high-dimensional spatial-aware representations. Comprehensive experiments on 14 datasets demonstrate that ST-LLM achieves comparable or better performance than traditional model. ST-LLM shows that LLMs embeddings provide a new simple and effective path to encoding spatial transcriptomics biological knowledge.

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