Efficient In-Training Adaptive Compound Loss Function Contribution Control for Medical Image Segmentation

Image segmentation plays a crucial role in many clinical applications, including disease diagnosis and monitoring. Current state-of-the-art segmentation approaches use deep neural networks that are trained on their target tasks by minimizing a loss function. Class imbalance is one of the major challenges that these networks face, where the target object is significantly underrepresented. Compound loss functions that incorporate the binary cross-entropy (BCE) and Dice loss are among the most prominent approaches to address this issue. However, determining the contribution of each individual loss to the overall compound loss function is a tedious process. It requires hyperparameter fine-tuning and multiple iterations of training, which is highly inefficient in terms of time and energy consumption. To address this issue, we propose an approach that adaptively controls the contribution of each of these individual loss functions during training. This eliminates the need for multiple fine-tuning iterations to achieve the desired precision and recall for segmentation models.

I. Introduction

Image segmentation is the process of partitioning an image into different regions that belong to different objects of interest based on the characteristics of these regions [1]. It is an essential computer vision task that is used for many applications such as autonomous driving, face recognition, industrial quality control, and medical image analysis. In medical image analysis, segmentation is used to detect anatomical structures of interest as well as anomalies in the scanned body such as lesions for cancer detection and treatment planning [2].