# A deep learning pipeline for breast cancer ki-67 proliferation index scoring

arXiv: https://arxiv.org/abs/2203.07452 · HTML: https://mlsanity.com/paper/2203.07452/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 65 references that are in the map.
- First posted on arXiv 14 Mar 2022.

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

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation](https://mlsanity.com/paper/1707.03718/) (2017)
- [The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation](https://mlsanity.com/paper/1611.09326/) (2016)
- [Machine learning methods for histopathological image analysis](https://mlsanity.com/paper/1709.00786/) (2017)
- [Methods for Segmentation and Classification of Digital Microscopy Tissue Images](https://mlsanity.com/paper/1810.13230/) (2018)
- [DeepSDCS: Dissecting cancer proliferation heterogeneity in Ki67 digital whole slide images](https://mlsanity.com/paper/1806.10850/) (2018)
- [A Survey of Breast Cancer Screening Techniques: Thermography and Electrical Impedance Tomography](https://mlsanity.com/paper/2202.03737/) (2022)

## Abstract (arXiv)

The Ki-67 proliferation index is an essential biomarker that helps pathologists to diagnose and select appropriate treatments. However, automatic evaluation of Ki-67 is difficult due to nuclei overlapping and complex variations in their properties. This paper proposes an integrated pipeline for accurate automatic counting of Ki-67, where the impact of nuclei separation techniques is highlighted. First, semantic segmentation is performed by combining the Squeez and Excitation Resnet and Unet algorithms to extract nuclei from the background. The extracted nuclei are then divided into overlapped and non-overlapped regions based on eight geometric and statistical features. A marker-based Watershed algorithm is subsequently proposed and applied only to the overlapped regions to separate nuclei. Finally, deep features are extracted from each nucleus patch using Resnet18 and classified into positive or negative by a random forest classifier. The proposed pipeline's performance is validated on a dataset from the Department of Pathology at Hopital Nord Franche-Comt'e hospital.