A Multi-Scale Interpretability-Based PET-CT Tumor Segmentation Method

Dangui Yang, Yetong Wang, Yimeng Ma and Houqun Yang ()
Additional contact information
Dangui Yang: School of Computer Science and Technology, Hainan University, Haikou 570228, China
Yetong Wang: Hainan Engineering Research Center for Virtual Reality Technology and Systems, Hainan Vocational University of Science and Technology, Haikou 571126, China
Yimeng Ma: School of Computer Science and Technology, Hainan University, Haikou 570228, China
Houqun Yang: School of Computer Science and Technology, Hainan University, Haikou 570228, China

Mathematics, 2025, vol. 13, issue 7, 1-22

Abstract: Accurate tumor segmentation is crucial for clinical diagnosis, treatment planning, and efficacy evaluation in medical imaging. Although traditional image processing techniques have been widely applied in tumor segmentation, they often perform poorly when dealing with tumors that have low contrast, irregular shapes, or varying sizes. With the rise of deep learning, particularly the application of convolutional neural networks (CNNs) in medical image segmentation, significant progress has been made, especially in handling multimodal data such as positron emission tomography (PET) and computed tomography (CT). However, the “black-box” nature of CNNs presents challenges for interpretability, which is particularly important in clinical applications. To address this, we propose a tumor segmentation framework based on a multi-scale interpretability module (MSIM). Through ablation experiments and comparisons on three public datasets, we evaluate the performance of the proposed method. The ablation results show that the proposed method achieves an improvement of 1.6, 1.62, and 2.36 in the Dice Similarity Coefficient (DSC) on the Melanoma, Lymphoma, and Lung Cancer datasets, respectively, highlighting the benefits of the interpretability module. Furthermore, the method outperforms the best comparative methods on all three datasets, achieving DSC improvements of 1.46, 1.27, and 1.93, respectively. Finally, visualization and perturbation experiments further validate the effectiveness of our method in emphasizing critical features.

Keywords: tumor segmentation; PET-CT; interpretability; deep learning; cancer (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/7/1139/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/7/1139/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:7:p:1139-:d:1624484

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().