Data Augmentation Improvement for a Breast Cancer Dataset: Fine-Tuning Bounding Box Coordinates and Segmentation Mask

Abstract

Abstract Breast cancer is one of the leading causes of death among women worldwide, and early detection through medical imaging techniques is crucial for effective treatment. Deep learning models have shown promising results in medical image analysis tasks, but traditional data augmentation methods often do not preserve the accuracy of bounding box and segmentation mask annotations. To address this issue, a proposed method for fine-tuning new coordinates of bounding box and segmentation mask during data augmentation methods cropping and rotation in the breast cancer dataset has been introduced. This method involves generating new images by applying cropping and rotation to the original images and adjusting the coordinates of the bounding box and segmentation mask to match the new image. Experiments conducted on a publicly available breast cancer dataset showed that the proposed method improved the accuracy of the bounding box and segmentation mask annotations while preserving the original information in the image. The proposed method is a promising approach to improve the accuracy of deep learning models for medical image analysis tasks. By dynamically adjusting the coordinates during augmentation, the proposed method can better preserve object shape and improve the accuracy of object detection and segmentation tasks. The approach can be easily integrated into existing data augmentation pipelines and has the potential to improve performance on a range of computer vision applications.