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Linghan Cai

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AAAI Conference 2026 Conference Paper

PathFLIP: Fine-grained Language-Image Pretraining for Versatile Computational Pathology

Fengchun Liu
Songhan Jiang
Linghan Cai
Ziyue Wang
Yongbing Zhang

While Vision-Language Models (VLMs) have achieved notable progress in computational pathology (CPath), the gigapixel scale and spatial heterogeneity of Whole Slide Images (WSIs) continue to pose challenges for multimodal understanding. Existing alignment methods struggle to capture fine-grained correspondences between textual descriptions and visual cues across thousands of patches from a slide, compromising their performance on downstream tasks. In this paper, we propose PathFLIP (Pathology Fine-grained Language-Image Pretraining), a novel framework for holistic WSI interpretation. PathFLIP decomposes slide-level captions into region-level sub-captions and generates text-conditioned region embeddings to facilitate precise visual-language grounding. By harnessing Large Language Models (LLMs), PathFLIP can seamlessly follow diverse clinical instructions and adapt to varied diagnostic contexts. Furthermore, it exhibits versatile capabilities across multiple paradigms, efficiently handling slide-level classification and retrieval, fine-grained lesion localization, and instruction following. Extensive experiments demonstrate that PathFLIP outperforms existing large-scale pathological VLMs on four representative benchmarks while requiring significantly less training data, paving the way for fine-grained, instruction-aware WSI interpretation in research and clinical practice.

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JBHI Journal 2025 Journal Article

SEINE: Structure Encoding and Interaction Network for Nuclei Instance Segmentation

Ye Zhang
Linghan Cai
Ziyue Wang
Yongbing Zhang

Nuclei instance segmentation in histopathological images is crucial for biological analysis and cancer diagnosis. However, it faces two significant challenges: (1) poorly stained nuclei can lead to under-segmentation, as the background may be mistakenly identified as the foreground; and (2) deep textures within nuclei often result in fragmented instance predictions, as these textures can be misinterpreted as contours. To address these problems, this paper proposes a Structure Encoding and Interaction NEtwork, termed SEINE, which develops the nuclei structure modeling scheme and takes advantage of the similarity between nuclei structure to improve the integrality of instance segmentation. Specifically, SEINE introduces a contour-based structure encoding mechanism that integrates the correlation between nuclear structure and semantics, enabling a more accurate structural representation. Building on this encoding, we propose a structure-guided attention module, which uses clear nuclei as prototypes to guide the structural learning of unclear nuclei, thereby addressing the under-segmentation problem. Additionally, a position enhancement strategy applies a centroid distance constraint to reduce contour prediction errors, effectively mitigating fragmented instance segmentation. Extensive experiments demonstrate the effectiveness of SEINE, achieving state-of-the-art performance across four benchmark datasets.

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