# Conditional Deep Convolutional Neural Networks for Improving the Automated Screening of Histopathological Images

arXiv: https://arxiv.org/abs/2105.14338 · HTML: https://mlsanity.com/paper/2105.14338/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 29 May 2021.

## Builds on (most cited first, 14 of 14)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [Transfusion: Understanding Transfer Learning for Medical Imaging](https://mlsanity.com/paper/1902.07208/) (2019)
- [Deep neural network models for computational histopathology: A survey](https://mlsanity.com/paper/1912.12378/) (2019)
- [Data augmentation using learned transformations for one-shot medical image segmentation](https://mlsanity.com/paper/1902.09383/) (2019)
- [Machine learning methods for histopathological image analysis](https://mlsanity.com/paper/1709.00786/) (2017)
- ['Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images](https://mlsanity.com/paper/1902.01314/) (2019)
- [Deep Learning for Whole Slide Image Analysis: An Overview](https://mlsanity.com/paper/1910.11097/) (2019)
- [Semi-supervised few-shot learning for medical image segmentation](https://mlsanity.com/paper/2003.08462/) (2020)
- [Few-Shot Segmentation Propagation with Guided Networks](https://mlsanity.com/paper/1806.07373/) (2018)
- [A Closer Look at Domain Shift for Deep Learning in Histopathology](https://mlsanity.com/paper/1909.11575/) (2019)
- [Mixed-Supervised Dual-Network for Medical Image Segmentation](https://mlsanity.com/paper/1907.10209/) (2019)

## Abstract (arXiv)

Semantic segmentation of breast cancer metastases in histopathological slides is a challenging task. In fact, significant variation in data characteristics of histopathology images (domain shift) make generalization of deep learning to unseen data difficult. Our goal is to address this challenge by using a conditional Fully Convolutional Network (co-FCN) whose output can be conditioned at run time, and which can improve its performance when a properly selected set of reference slides are used to condition the output. We adapted to our task a co-FCN originally applied to organs segmentation in volumetric medical images and we trained it on the Whole Slide Images (WSIs) from three out of five medical centers present in the CAMELYON17 dataset. We tested the performance of the network on the WSIs of the remaining centers. We also developed an automated selection strategy for selecting the conditioning subset, based on an unsupervised clustering process applied to a target-specific set of reference patches, followed by a selection policy that relies on the cluster similarities with the input patch. We benchmarked our proposed method against a U-Net trained on the same dataset with no conditioning. The conditioned network shows better performance that the U-Net on the WSIs with Isolated Tumor Cells and micro-metastases from the medical centers used as test. Our contributions are an architecture which can be applied to the histopathology domain and an automated procedure for the selection of conditioning data.