# Cracking the PUMA Challenge in 24 Hours with CellViT++ and nnU-Net

arXiv: https://arxiv.org/abs/2503.12269 · HTML: https://mlsanity.com/paper/2503.12269/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 5 references that are in the map.
- First posted on arXiv 15 Mar 2025.

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

- [CellViT: Vision Transformers for Precise Cell Segmentation and Classification](https://mlsanity.com/paper/2306.15350/) (2023)
- [CellViT++: Energy-Efficient and Adaptive Cell Segmentation and Classification Using Foundation Models](https://mlsanity.com/paper/2501.05269/) (2025)

## Abstract (arXiv)

Automatic tissue segmentation and nuclei detection is an important task in pathology, aiding in biomarker extraction and discovery. The panoptic segmentation of nuclei and tissue in advanced melanoma (PUMA) challenge aims to improve tissue segmentation and nuclei detection in melanoma histopathology. Unlike many challenge submissions focusing on extensive model tuning, our approach emphasizes delivering a deployable solution within a 24-hour development timeframe, using out-of-the-box frameworks. The pipeline combines two models, namely CellViT++ for nuclei detection and nnU-Net for tissue segmentation. Our results demonstrate a significant improvement in tissue segmentation, achieving a Dice score of 0.750, surpassing the baseline score of 0.629. For nuclei detection, we obtained results comparable to the baseline in both challenge tracks. The code is publicly available at https://github.com/TIO-IKIM/PUMA.