Image Annotation Platform for Training Dataset in Endometriosis Automatic Detection

Abstract

Endometriosis is a prevalent gynecological condition affecting millions of women worldwide. The accurate and early diagnosis of endometriosis is crucial for effective management and improved patient outcomes. Machine learning and deep learning techniques have shown promising results in automating the detection of endometriotic lesions from medical images. However, these models heavily rely on high-quality, accurately annotated training datasets. This paper introduces an innovative image annotation platform specifically designed for creating comprehensive training datasets to enhance the performance of endometriosis automatic detection models. The platform offers a range of tools and features tailored to the unique challenges posed by endometriotic lesion annotation, including the diverse appearance of lesions and variations in imaging modalities. By harnessing this annotation platform, researchers and medical practitioners can efficiently create large, high-quality training datasets for endometriosis automatic detection models, thus advancing the development of accurate, efficient, and reliable tools for early diagnosis and treatment planning. We anticipate that the platform will catalyze further research in the field of endometriosis detection and contribute to improved patient care and outcomes.