Background Introduction In the field of machine learning, Semi-Supervised Learning (SSL) has garnered significant attention due to its ability to leverage a small amount of labeled data and a large amount of unlabeled data for learning. Particularly in scenarios where data labeling is costly, graph-based semi-supervised learning methods have become...

Academic Background and Research Motivation In the field of dental healthcare, Cone Beam Computed Tomography (CBCT) is a widely used three-dimensional imaging technique. CBCT provides three-dimensional images of the oral cavity and is particularly effective in diagnosing odontogenic lesions. However, the segmentation of CBCT images—labeling each vo...

Academic Background and Problem Statement With the increasing prominence of data privacy issues, Federated Learning (FL) has emerged as a decentralized machine learning paradigm, allowing multiple clients to collaboratively train a global model without sharing data, thereby protecting data privacy. However, existing FL methods typically assume that...

Report on the Paper “PICK: Predict and Mask for Semi-Supervised Medical Image Segmentation” Academic Background Accurate segmentation of medical images is crucial in clinical practice, as it provides vital insights into organ/tumor characteristics such as volume, location, and shape. Recent studies have highlighted the significant potential of data...

Academic Paper Report: Open World Object Detection Framework Using SAM Background As the importance of ecosystem monitoring grows, the observation of wildlife and plant populations has become a crucial aspect of ecological conservation and agricultural development. These monitoring tasks include estimating population sizes, identifying species, stu...