# Exploiting Spatial Structure for Transductive Few-Shot Classification of Whole-Slide Images

arXiv: https://arxiv.org/abs/2609.31040 · HTML: https://mlsanity.com/paper/2609.31040/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 20 references that are in the map.
- First posted on arXiv 25 Sep 2026.

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

- [Quilt-1M: One Million Image-Text Pairs for Histopathology](https://mlsanity.com/paper/2306.11207/) (2023)
- [BACH: Grand Challenge on Breast Cancer Histology Images](https://mlsanity.com/paper/1808.04277/) (2018)
- [Boosting Vision-Language Models with Transduction](https://mlsanity.com/paper/2406.01837/) (2024)
- [Realistic Evaluation of Transductive Few-Shot Learning](https://mlsanity.com/paper/2204.11181/) (2022)
- [Few-shot Adaptation of Medical Vision-Language Models](https://mlsanity.com/paper/2409.03868/) (2024)
- [Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset](https://mlsanity.com/paper/2201.11446/) (2022)
- [Boosting Vision-Language Models for Histopathology Classification: Predict all at once](https://mlsanity.com/paper/2409.01883/) (2024)
- [How Good is my Histopathology Vision-Language Foundation Model? A Holistic Benchmark](https://mlsanity.com/paper/2503.12990/) (2025)
- [A transductive few-shot learning approach for classification of digital histopathological slides from liver cancer](https://mlsanity.com/paper/2311.17740/) (2023)
- [Conditional Random Fields for Interactive Refinement of Histopathological Predictions](https://mlsanity.com/paper/2601.12082/) (2026)
- [Whole-Slide Image Analysis under Realistic Few-Shot Annotation Protocols](https://mlsanity.com/paper/2608.30420/) (2026)
- [Locally Consistent Transductive Information Maximization for Few-Shot Remote Sensing Scene Classification](https://mlsanity.com/paper/2607.29192/) (2026)

## Abstract (arXiv)

Automating the analysis of whole-slide images (WSIs), a key step in cancer diagnosis, has high clinical value, as it can reduce pathologist's workload while improving diagnosis accuracy. Recently, vision-language models have shown promising performance for patch-level classification without requiring any annotation, yet these zero-shot (ZS) predictions remain noisy on fine-grained tasks and must be further refined. A promising direction is to refine all predictions jointly, i.e., a transductive approach. However, most existing methods are not tailored to WSIs. We thus propose SlideTIM, an adaptation to WSIs of the recent transductive approach LC-TIM, which introduces a combined spatial--latent regularizer together with a prior on the patch class distribution. The former enforces spatially and semantically close patches to receive the same predictions, while the prior calibrates the predicted class proportions. Together, they address the complex spatial organization and the strong class imbalance of WSIs. Evaluated on four histology datasets, SlideTIM consistently outperforms all TIM variants, improving the macro-F1 by +8.1pp over the best competing baseline at 1 shot. Compared to the ZS, it raises the macro-F1 by +19.4pp at 1 shot. The code will be made available after submission.