# FTU-Seek: Foundation Model-Guided Hard-Negative Learning for Sparse Functional Tissue Unit Segmentation

arXiv: https://arxiv.org/abs/2609.00704 · HTML: https://mlsanity.com/paper/2609.00704/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 73 references that are in the map.
- First posted on arXiv 1 Sep 2026.

## Builds on (most cited first, 12 of 12)

- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017)
- [Training Region-based Object Detectors with Online Hard Example Mining](https://mlsanity.com/paper/1604.03540/) (2016)
- [Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest](https://mlsanity.com/paper/1603.00275/) (2016)
- [Automated Gleason Grading of Prostate Biopsies using Deep Learning](https://mlsanity.com/paper/1907.07980/) (2019)
- [A Clinical Benchmark of Public Self-Supervised Pathology Foundation Models](https://mlsanity.com/paper/2407.06508/) (2024)
- [HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images](https://mlsanity.com/paper/2006.12230/) (2020)
- [Deep Instance-Level Hard Negative Mining Model for Histopathology Images](https://mlsanity.com/paper/1906.09681/) (2019)
- [Hard Negative Sample Mining for Whole Slide Image Classification](https://mlsanity.com/paper/2410.02212/) (2024)
- [Age-Structured Harvesting Models: A Structural Comparison of Rate-Control and Effort-Control Optimality Systems](https://mlsanity.com/paper/2603.09767/) (2026)
- [Size-Selective Threshold Harvesting under Nonlocal Crowding and Exogenous Recruitment](https://mlsanity.com/paper/2603.27368/) (2026)

## Abstract (arXiv)

Functional tissue units (FTUs), including tertiary lymphoid structures (TLSs), blood vessels, and glands, encode localized immune, vascular, and epithelial organization in histopathology. Accurate quantification of these structures is important for studying tissue architecture and disease-associated tissue organization. However, FTUs are frequently sparse, heterogeneous, and surrounded by large amounts of morphologically similar background tissue, making automated segmentation in whole-slide images (WSIs) challenging. We therefore developed FTU-Seek, a pathology foundation model-guided framework that treats morphology-aware negative-patch selection as a key component of sparse FTU segmentation. FTU-Seek uses frozen multi-depth features from the UNI pathology foundation model to train a patch-level classifier that distinguishes FTU-containing from FTU-absent tissue. Target-absent patches are subsequently ranked according to their predicted target-containing probabilities, and the highest-scoring hard negatives are selected through a static TopK strategy to construct compact segmentation training sets. The framework was evaluated using five-fold cross-validation and internal test cohorts across TLS, blood-vessel, and gland segmentation tasks, with an additional independent 30-WSI held-out cohort for TLS. Positive-only, all-tissue, random-negative, and matched random TopK sampling strategies served as comparators. Segmentation-derived phenotypes were further explored in external TCGA cohorts.