SegTME-UNI2：基于基础模型的组织病理学多类细胞分割与 LLM 驱动肿瘤微环境表征框架

Computer Science > Computer Vision and Pattern Recognition arXiv:2606.17702 (cs) [Submitted on 16 Jun 2026 (v1), last revised 16 Sep 2026 (this version, v3)] Title:SegTME-UNI2: A Foundation Model-Based Framework for Generalisable Multiclass Cell Segmentation and LLM-Driven Tumour Microenvironment Characterisation in Histopathology Authors:Wan Siti Halimatul Munirah Wan Ahmad, Faris Syahmi Samidi, Mohammad Badal Ahmmed, Vimal Angela Thiviyanathan, Selvam Thavaraj, Anwar P.P. Abdul Majeed View a PDF of the paper titled SegTME-UNI2: A Foundation Model-Based Framework for Generalisable Multiclass Cell Segmentation and LLM-Driven Tumour Microenvironment Characterisation in Histopathology, by Wan Siti Halimatul Munirah Wan Ahmad and 4 other authors View PDF HTML (experimental) Abstract:Characterising the TME from routine H&E-stained histology images requires simultaneous cell segmentation, biological feature extraction, and interpretable clinical reporting. We present SegTME-UNI2, a unified framework addressing all three requirements end-to-end: a segmentation backbone that converts raw H\&E patches into per-nucleus class labels, a structured feature-extraction pipeline that turns those labels into quantitative TME descriptors, and a language-model narrative generator that turns those descriptors into clinician-readable text. At its core is UNI2-UperHoVer, a dual-head multiscale segmentation model that pairs UNI2 with two parallel UperNet decoders: one for six-class semantic segmentation and one for HV gradient regression enabling watershed-based nuclear instance separation. It is trained via a three-stage progressive pseudo-label curriculum, scaling from PanNuke (Stage 1, 0.25um/pixel) to TCGA-UT Scale-0 (Stage 2, 0.5um/pixel) and full 1.6M-patch, six-scale TCGA-UT (Stage 3, 0.5 to 1.0um/pixel). TCGA-UT's coarser, broader per-patch context than PanNuke's also permits a larger tile stride during whole-slide inference. This pipeline computes 22 per-patch compositional, morphological, spatial-entropy, and intercellular-distance metrics and translates them into six categorical phenotype labels and a standardised biological-token vocabulary, fine-tuned via NVIDIA BioNeMo that converts into clinically grounded narratives whose individual claims can be spot-checked directly against the underlying features. Qualitative validation on IGNITE NSCLC tiles shows the pipeline produces biologically coherent phenotype classifications and narratives despite inter-institutional stain variability and imperfect segmentation. The pseudo-labelled TCGA-UT dataset and UNI2-UperHoVer checkpoints are publicly released to support large-scale TME profiling and spatial biology research. Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) Cite as: arXiv:2606.17702 [cs.CV] (or arXiv:2606.17702v3 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2606.17702 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Wan Siti Halimatul Munirah Wan Ahmad [view email] [v1] Tue, 16 Jun 2026 09:12:19 UTC (45,451 KB) [v2] Sun, 21 Jun 2026 10:11:42 UTC (33,344 KB) [v3] Wed, 16 Sep 2026 09:23:23 UTC (16,600 KB) Full-text links: Access Paper: View a PDF of the paper titled SegTME-UNI2: A Foundation Model-Based Framework for Generalisable Multiclass Cell Segmentation and LLM-Driven Tumour Microenvironment Characterisation in Histopathology, by Wan Siti Halimatul Munirah Wan Ahmad and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV new | recent | 2026-06 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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