Addressing Class Imbalance Problem in Semantic Segmentation Using Binary Focal Loss

Image segmentation is a foundational technique in computer vision with wide-ranging applications, including its critical role in medical imaging for object identification, automatic labeling, and disease diagnosis. Advancements in deep learning have significantly improved the accuracy and efficiency of image segmentation, making it an increasingly valuable tool in various domains. Class imbalanced datasets are a frequent problem experienced when trying to train segmentation networks. Class imbalance occurs when some classes (semantic categories) in the image have significantly more instances (pixels) than others. In semantic segmentation, this often happens because certain object categories are more prevalent in the real world or dataset, while others are rarer. When training a deep learning model for semantic segmentation, this imbalance can lead to several problems. In this article, we have experimented with the class weightage parameters of binary focal loss to address the class imbalance problem in semantic segmentation. By utilizing the CANDID-PTX dataset, we have utilized U-Net architecture containing upsampling (encoder) and a downsampling (decoder) network for comparing binary focal loss rates among different alpha and gamma coefficients class weights. Doing so, we found that the adjustment of class weights in the loss function could notably help in resolving the class imbalance problems.