# Renal Cell Carcinoma Detection and Subtyping with Minimal Point-Based Annotation in Whole-Slide Images

arXiv: https://arxiv.org/abs/2008.05332 · HTML: https://mlsanity.com/paper/2008.05332/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2020.

## Built on by (most cited first, 12 of 12)

- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [Recent advances and clinical applications of deep learning in medical image analysis](https://mlsanity.com/paper/2105.13381/) (2021)
- [Multi-Layer Pseudo-Supervision for Histopathology Tissue Semantic Segmentation using Patch-level Classification Labels](https://mlsanity.com/paper/2110.08048/) (2021)
- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [Instance-based Vision Transformer for Subtyping of Papillary Renal Cell Carcinoma in Histopathological Image](https://mlsanity.com/paper/2106.12265/) (2021)
- [Weakly supervised segmentation with point annotations for histopathology images via contrast-based variational model](https://mlsanity.com/paper/2304.03572/) (2023)
- [Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network](https://mlsanity.com/paper/2106.10641/) (2021)
- [Beyond Pixel-Wise Supervision for Medical Image Segmentation: From Traditional Models to Foundation Models](https://mlsanity.com/paper/2404.13239/) (2024)
- [A Personalized Diagnostic Generation Framework Based on Multi-source Heterogeneous Data](https://mlsanity.com/paper/2110.13677/) (2021)
- [PIMIP: An Open Source Platform for Pathology Information Management and Integration](https://mlsanity.com/paper/2111.05794/) (2021)
- [Tumor segmentation on whole slide images: training or prompting?](https://mlsanity.com/paper/2402.13932/) (2024)
- [Renal Cell Carcinoma subtyping: learning from multi-resolution localization](https://mlsanity.com/paper/2411.09471/) (2024)

## Abstract (arXiv)

Obtaining a large amount of labeled data in medical imaging is laborious and time-consuming, especially for histopathology. However, it is much easier and cheaper to get unlabeled data from whole-slide images (WSIs). Semi-supervised learning (SSL) is an effective way to utilize unlabeled data and alleviate the need for labeled data. For this reason, we proposed a framework that employs an SSL method to accurately detect cancerous regions with a novel annotation method called Minimal Point-Based annotation, and then utilize the predicted results with an innovative hybrid loss to train a classification model for subtyping. The annotator only needs to mark a few points and label them are cancer or not in each WSI. Experiments on three significant subtypes of renal cell carcinoma (RCC) proved that the performance of the classifier trained with the Min-Point annotated dataset is comparable to a classifier trained with the segmentation annotated dataset for cancer region detection. And the subtyping model outperforms a model trained with only diagnostic labels by 12% in terms of f1-score for testing WSIs.