# Adaptive t-vMF Dice Loss for Multi-class Medical Image Segmentation

arXiv: https://arxiv.org/abs/2207.07842 · HTML: https://mlsanity.com/paper/2207.07842/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2022.

## Built on by (most cited first, 7 of 7)

- [SAR-RARP50: Segmentation of surgical instrumentation and Action Recognition on Robot-Assisted Radical Prostatectomy Challenge](https://mlsanity.com/paper/2401.00496/) (2023)
- [SACNet: A Spatially Adaptive Convolution Network for 2D Multi-organ Medical Segmentation](https://mlsanity.com/paper/2407.10157/) (2024)
- [Med-IC: Fusing a Single Layer Involution with Convolutions for Enhanced Medical Image Classification and Segmentation](https://mlsanity.com/paper/2409.18506/) (2024)
- [crossMoDA Challenge: Evolution of Cross-Modality Domain Adaptation Techniques for Vestibular Schwannoma and Cochlea Segmentation from 2021 to 2023](https://mlsanity.com/paper/2506.12006/) (2025)
- [CFPFormer: Feature-pyramid like Transformer Decoder for Segmentation and Detection](https://mlsanity.com/paper/2404.15451/) (2024)
- [Multiplicative Loss for Enhancing Semantic Segmentation in Medical and Cellular Images](https://mlsanity.com/paper/2510.12258/) (2025)
- [CLFSeg: A Fuzzy-Logic based Solution for Boundary Clarity and Uncertainty Reduction in Medical Image Segmentation](https://mlsanity.com/paper/2510.24202/) (2025)

## Abstract (arXiv)

Dice loss is widely used for medical image segmentation, and many improvement loss functions based on such loss have been proposed. However, further Dice loss improvements are still possible. In this study, we reconsidered the use of Dice loss and discovered that Dice loss can be rewritten in the loss function using the cosine similarity through a simple equation transformation. Using this knowledge, we present a novel t-vMF Dice loss based on the t-vMF similarity instead of the cosine similarity. Based on the t-vMF similarity, our proposed Dice loss is formulated in a more compact similarity loss function than the original Dice loss. Furthermore, we present an effective algorithm that automatically determines the parameter kappa for the t-vMF similarity using a validation accuracy, called Adaptive t-vMf Dice loss. Using this algorithm, it is possible to apply more compact similarities for easy classes and wider similarities for difficult classes, and we are able to achieve an adaptive training based on the accuracy of the class. Through experiments conducted on four datasets using a five-fold cross validation, we confirmed that the Dice score coefficient (DSC) was further improved in comparison with the original Dice loss and other loss functions.