# Cancer Metastasis Detection With Neural Conditional Random Field

arXiv: https://arxiv.org/abs/1806.07064 · HTML: https://mlsanity.com/paper/1806.07064/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2018.

## Built on by (most cited first, 17 of 17)

- [DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification](https://mlsanity.com/paper/2203.12081/) (2022)
- [BMAD: Benchmarks for Medical Anomaly Detection](https://mlsanity.com/paper/2306.11876/) (2023)
- [Attention-Challenging Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2311.07125/) (2023)
- [Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2403.07939/) (2024)
- [SaccadeDet: A Novel Dual-Stage Architecture for Rapid and Accurate Detection in Gigapixel Images](https://mlsanity.com/paper/2407.17956/) (2024)
- [Dataset Distillation for Histopathology Image Classification](https://mlsanity.com/paper/2408.09709/) (2024)
- [Conditional Random Fields for Interactive Refinement of Histopathological Predictions](https://mlsanity.com/paper/2601.12082/) (2026)
- [Whole-slide-imaging Cancer Metastases Detection and Localization with Limited Tumorous Data](https://mlsanity.com/paper/2303.10342/) (2023)
- [Magnifying Networks for Images with Billions of Pixels](https://mlsanity.com/paper/2112.06121/) (2021)
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- [Imperceptible Adversarial Examples for Fake Image Detection](https://mlsanity.com/paper/2106.01615/) (2021)
- [EVAC+: Multi-scale V-net with Deep Feature CRF Layers for Brain Extraction](https://mlsanity.com/paper/2206.02837/) (2022)
- [Open-World Object Detection via Discriminative Class Prototype Learning](https://mlsanity.com/paper/2302.11757/) (2023)
- [Distilling High Diagnostic Value Patches for Whole Slide Image Classification Using Attention Mechanism](https://mlsanity.com/paper/2407.19821/) (2024)
- [Comparative clinical evaluation of "memory-efficient" synthetic 3d generative adversarial networks (gan) head-to-head to state of art: results on computed tomography of the chest](https://mlsanity.com/paper/2501.15572/) (2025)
- [Memory-Efficient 3D High-Resolution Medical Image Synthesis Using CRF-Guided GANs](https://mlsanity.com/paper/2503.10899/) (2025)
- [FeTTL: Federated Template and Task Learning for Multi-Institutional Medical Imaging](https://mlsanity.com/paper/2601.16302/) (2026)

## Abstract (arXiv)

Breast cancer diagnosis often requires accurate detection of metastasis in lymph nodes through Whole-slide Images (WSIs). Recent advances in deep convolutional neural networks (CNNs) have shown significant successes in medical image analysis and particularly in computational histopathology. Because of the outrageous large size of WSIs, most of the methods divide one slide into lots of small image patches and perform classification on each patch independently. However, neighboring patches often share spatial correlations, and ignoring these spatial correlations may result in inconsistent predictions. In this paper, we propose a neural conditional random field (NCRF) deep learning framework to detect cancer metastasis in WSIs. NCRF considers the spatial correlations between neighboring patches through a fully connected CRF which is directly incorporated on top of a CNN feature extractor. The whole deep network can be trained end-to-end with standard back-propagation algorithm with minor computational overhead from the CRF component. The CNN feature extractor can also benefit from considering spatial correlations via the CRF component. Compared to the baseline method without considering spatial correlations, we show that the proposed NCRF framework obtains probability maps of patch predictions with better visual quality. We also demonstrate that our method outperforms the baseline in cancer metastasis detection on the Camelyon16 dataset and achieves an average FROC score of 0.8096 on the test set. NCRF is open sourced at https://github.com/baidu-research/NCRF.