# Single Shot AI-assisted quantification of KI-67 proliferation index in breast cancer

arXiv: https://arxiv.org/abs/2503.19606 · HTML: https://mlsanity.com/paper/2503.19606/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 25 Mar 2025.

## Builds on (most cited first, 2 of 2)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)

## Abstract (arXiv)

Reliable quantification of Ki-67, a key proliferation marker in breast cancer, is essential for molecular subtyping and informed treatment planning. Conventional approaches, including visual estimation and manual counting, suffer from interobserver variability and limited reproducibility. This study introduces an AI-assisted method using the YOLOv8 object detection framework for automated Ki-67 scoring. High-resolution digital images (40x magnification) of immunohistochemically stained tumor sections were captured from Ki-67 hotspot regions and manually annotated by a domain expert to distinguish Ki-67-positive and negative tumor cells. The dataset was augmented and divided into training (80%), validation (10%), and testing (10%) subsets. Among the YOLOv8 variants tested, the Medium model achieved the highest performance, with a mean Average Precision at 50% Intersection over Union (mAP50) exceeding 85% for Ki-67-positive cells. The proposed approach offers an efficient, scalable, and objective alternative to conventional scoring methods, supporting greater consistency in Ki-67 evaluation. Future directions include developing user-friendly clinical interfaces and expanding to multi-institutional datasets to enhance generalizability and facilitate broader adoption in diagnostic practice.