# Model Checking in Medical Imaging for Tumor Detection and Segmentation

arXiv: https://arxiv.org/abs/2501.02024 · HTML: https://mlsanity.com/paper/2501.02024/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 51 references that are in the map.
- First posted on arXiv 2 Jan 2025.

## Builds on (most cited first, 7 of 7)

- [Deep Learning Ensembles for Melanoma Recognition in Dermoscopy Images](https://mlsanity.com/paper/1610.04662/) (2016)
- [Monitoring Mobile and Spatially Distributed Cyber-Physical Systems](https://mlsanity.com/paper/1904.08847/) (2019)
- [Specifying and Verifying Properties of Space - Extended Version](https://mlsanity.com/paper/1406.6393/) (2014)
- [Qualitative and Quantitative Monitoring of Spatio-Temporal Properties with SSTL](https://mlsanity.com/paper/1706.09334/) (2017)
- [VoxLogicA: a Spatial Model Checker for Declarative Image Analysis (Extended Version)](https://mlsanity.com/paper/1811.05677/) (2018)
- [Spatial Logics and Model Checking for Medical Imaging (Extended Version)](https://mlsanity.com/paper/1811.06065/) (2018)
- [From Collective Adaptive Systems to Human Centric Computation and Back: Spatial Model Checking for Medical Imaging](https://mlsanity.com/paper/1607.02235/) (2016)

## Abstract (arXiv)

Recent advancements in model checking have demonstrated significant potential across diverse applications, particularly in signal and image analysis. Medical imaging stands out as a critical domain where model checking can be effectively applied to design and evaluate robust frameworks. These frameworks facilitate automatic and semi-automatic delineation of regions of interest within images, aiding in accurate segmentation. This paper provides a comprehensive analysis of recent works leveraging spatial logic to develop operators and tools for identifying regions of interest, including tumorous and non-tumorous areas. Additionally, we examine the challenges inherent to spatial model-checking techniques, such as variability in ground truth data and the need for streamlined procedures suitable for routine clinical practice.