Основні поняття
Introducing the SM2C algorithm to enhance semi-supervised medical image segmentation by diversifying shape and enriching semantic information.
Анотація
The article introduces the SM2C algorithm to improve semi-supervised medical image segmentation. It discusses the challenges of limited labeled data in medical imaging and the potential of machine learning algorithms for accurate segmentation. The SM2C method focuses on scaling-up image size, multi-class mixing, and object shape jittering to enhance semantic feature learning within medical images. Extensive experiments demonstrate the effectiveness of SM2C on benchmark datasets, showing significant improvements over existing methods. The article also includes a detailed explanation of related work, dataset details, implementation specifics, evaluation metrics, comparison with other methods, and an ablation study.
Structure:
Introduction to Semantic Segmentation in Medical Imaging
Advances in supervised deep learning methods
Importance of image processing in medical assistance
Challenges in Medical Image Annotation
Laborious task requiring expertise
Semi-Supervised Learning for Medical Image Segmentation
Pseudo-labelling methods for extending datasets
Introduction of SM2C Algorithm
Scaling-up Mix with Multi-Class approach
Experimental Results on Benchmark Datasets
ACDC dataset results compared to existing methods
Ablation Study on Components of SM2C Algorithm
Статистика
"Extensive experiments demonstrate the effectiveness of the SM2C on three benchmark medical image segmentation datasets."
"The proposed framework shows significant improvements over state-of-the-art counterparts."
Цитати
"Scaling-up Mix creates images with increased foreground-background diversity by concatenating four images."
"Multi-Class Mix preserves the structural integrity of segmentation object contours by increasing the number of objects within input images."