  Deep learning has achieved outstanding accuracy in medical image
segmentation, particularly for objects like organs or tumors with smooth
boundaries or large sizes. Whereas, it encounters significant difficulties with
objects that have zigzag boundaries or are small in size, leading to a notable
decrease in segmentation effectiveness. In this context, using a loss function
that incorporates smoothness and volume information into a model's predictions
offers a promising solution to these shortcomings. In this work, we introduce
an Adaptive Focal Loss (A-FL) function designed to mitigate class imbalance by
down-weighting the loss for easy examples that results in up-weighting the loss
for hard examples and giving greater emphasis to challenging examples, such as
small and irregularly shaped objects. The proposed A-FL involves dynamically
adjusting a focusing parameter based on an object's surface smoothness, size
information, and adjusting the class balancing parameter based on the ratio of
targeted area to total area in an image. We evaluated the performance of the
A-FL using ResNet50-encoded U-Net architecture on the Picai 2022 and BraTS 2018
datasets. On the Picai 2022 dataset, the A-FL achieved an Intersection over
Union (IoU) of 0.696 and a Dice Similarity Coefficient (DSC) of 0.769,
outperforming the regular Focal Loss (FL) by 5.5% and 5.4% respectively. It
also surpassed the best baseline Dice-Focal by 2.0% and 1.2%. On the BraTS 2018
dataset, A-FL achieved an IoU of 0.883 and a DSC of 0.931. The comparative
studies show that the proposed A-FL function surpasses conventional methods,
including Dice Loss, Focal Loss, and their hybrid variants, in IoU, DSC,
Sensitivity, and Specificity metrics. This work highlights A-FL's potential to
improve deep learning models for segmenting clinically significant regions in
medical images, leading to more precise and reliable diagnostic tools.
