# TriDeNT: Triple Deep Network Training for Privileged Knowledge Distillation in Histopathology

arXiv: https://arxiv.org/abs/2312.02111 · HTML: https://mlsanity.com/paper/2312.02111/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Distillation for medical imaging, one of 12 directions in Knowledge distillation.
- First posted on arXiv 4 Dec 2023.

## Placed in

- Knowledge distillation / [Distillation for medical imaging](https://mlsanity.com/distill/distillation-for-medical-imaging/)

## Built on by (most cited first, 3 of 3)

- [Synthetic Privileged Information Enhances Medical Image Representation Learning](https://mlsanity.com/paper/2403.05220/) (2024)
- [Anatomy-Privileged Distillation with Token Routing for MRI-Based Prediction of Perineural Invasion](https://mlsanity.com/paper/2607.11987/) (2026)
- [Divide and Conquer Self-Supervised Learning for High-Content Imaging](https://mlsanity.com/paper/2503.07444/) (2025)

## Abstract (arXiv)

Computational pathology models rarely utilise data that will not be available for inference. This means most models cannot learn from highly informative data such as additional immunohistochemical (IHC) stains and spatial transcriptomics. We present TriDeNT, a novel self-supervised method for utilising privileged data that is not available during inference to improve performance. We demonstrate the efficacy of this method for a range of different paired data including immunohistochemistry, spatial transcriptomics and expert nuclei annotations. In all settings, TriDeNT outperforms other state-of-the-art methods in downstream tasks, with observed improvements of up to 101%. Furthermore, we provide qualitative and quantitative measurements of the features learned by these models and how they differ from baselines. TriDeNT offers a novel method to distil knowledge from scarce or costly data during training, to create significantly better models for routine inputs.