Decoding the Future of Biomedical Image Analysis: A Foundational Model for Multi-Modal Joint Segmentation, Detection, and Recognition Background In biomedical research, image analysis has become a crucial tool for advancing discoveries, enabling multi-scale studies ranging from organelles to organs. However, traditional biomedical image analysis of...

Overcoming the Preferred-Orientation Problem in Single-Particle Cryo-EM: An Innovative Solution through Deep Learning Background Introduction In recent years, single-particle cryogenic electron microscopy (Single-Particle Cryo-EM) has become a core technique in structural biology due to its ability to resolve the atomic-resolution structures of bio...

Artificial Intelligence and Terrestrial LiDAR Point Clouds in Forest Monitoring: Academic Report Academic Background With the increasing importance of global climate change and forest resource management, precision forestry has become a key direction in modern forest management. Precision forestry relies on high-precision forest data collection and...

Learning Meshing from Delaunay Triangulation for 3D Shape Representation Academic Background Surface reconstruction from point clouds is a long-standing problem in computer vision and graphics. Traditional implicit methods, such as Poisson surface reconstruction, compute an implicit function and extract the surface using the Marching Cubes algorith...

Dynamic People Tracking by Service Robots Using Diffusion Models Academic Background Tracking dynamic people in cluttered and crowded human-centered environments is a challenging problem in robotics. Due to intraclass variations such as occlusions, pose deformations, and lighting changes, traditional tracking methods often struggle to accurately id...

Academic Background and Problem Statement Multi-View Stereo (MVS) is a fundamental task in 3D computer vision that aims to recover the 3D geometry of a scene from multiple posed images. This technology has broad applications in robotics, scene understanding, augmented reality, and more. In recent years, learning-based MVS methods have achieved sign...

Academic Background and Problem Statement In recent years, deep neural networks have made significant progress in the field of computer vision, particularly in tasks such as image recognition, object detection, and semantic segmentation. However, even the most advanced deep models struggle when faced with long-tailed distribution data, where the nu...

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...

Academic Background and Problem Statement In recent years, deep learning models (Deep Neural Networks, DNNs) have achieved remarkable success in computer vision tasks. However, the training of these models relies heavily on large amounts of annotated data. When models are applied to new, unlabeled target domains, their generalization ability often ...

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...