# An Efficient Cervical Whole Slide Image Analysis Framework Based on Multi-scale Semantic and Location Deep Features

arXiv: https://arxiv.org/abs/2106.15113 · HTML: https://mlsanity.com/paper/2106.15113/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 40 references that are in the map.
- First posted on arXiv 29 Jun 2021.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks](https://mlsanity.com/paper/2009.11169/) (2020)
- [Detecting Cancer Metastases on Gigapixel Pathology Images](https://mlsanity.com/paper/1703.02442/) (2017)
- [DeepPap: Deep Convolutional Networks for Cervical Cell Classification](https://mlsanity.com/paper/1801.08616/) (2018)
- [Are pathologist-defined labels reproducible? Comparison of the TUPAC16 mitotic figure dataset with an alternative set of labels](https://mlsanity.com/paper/2007.05351/) (2020)
- [A Novel Automation-Assisted Cervical Cancer Reading Method Based on Convolutional Neural Network](https://mlsanity.com/paper/1912.06649/) (2019)
- [PFA-ScanNet: Pyramidal Feature Aggregation with Synergistic Learning for Breast Cancer Metastasis Analysis](https://mlsanity.com/paper/1905.01040/) (2019)

## Built on by (most cited first, 2 of 2)

- [Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime](https://mlsanity.com/paper/2302.05195/) (2023)
- [Cervical Glandular Cell Detection from Whole Slide Image with Out-Of-Distribution Data](https://mlsanity.com/paper/2205.14625/) (2022)

## Abstract (arXiv)

Digital gigapixel whole slide image (WSI) is widely used in clinical diagnosis, and automated WSI analysis is key for computer-aided diagnosis. Currently, analyzing the integrated descriptor of probabilities or feature maps from massive local patches encoded by ResNet classifier is the main manner for WSI-level prediction. Feature representations of the sparse and tiny lesion cells in cervical slides, however, are still challenging, while the unused location representations are available to supply the semantics classification. This study designs a novel and efficient framework with a new module InCNet constructed lightweight model YOLCO (You Only Look Cytology Once). It directly extracts feature inside the single cell (cluster) instead of the traditional way that from image tile with a fixed size. The InCNet (Inline Connection Network) enriches the multi-scale connectivity without efficiency loss. The proposal allows the input size enlarged to megapixel that can stitch the WSI by the average repeats decreased from 103~104 to 101~102 for collecting features and predictions at two scales. Based on Transformer for classifying the integrated multi-scale multi-task WSI features, the experimental results appear 0.872 AUC score better than the best conventional model on our dataset (n=2,019) from four scanners. The code is available at https://github.com/Chrisa142857/You-Only-Look-Cytopathology-Once , where the deployment version has the speed ~70 s/WSI.