Hoodline

MIT Unveils MultiverSeg, A Groundbreaking AI System to Revolutionize Medical Image Segmentation

MIT researchers developed an AI tool, MultiverSeg, to simplify the annotation of medical images for clinical research.
IEEE

DMoC-UNet: A Dynamic Mixture-of-Convolution Network for Enhanced Pathological Image Segmentation

Abstract: Pathological image segmentation is a cornerstone in medical image analysis and is crucial for tumor detection, tissue classification, and pathological diagnosis. Existing methods face ...

AI system for rapid annotation of medical images could accelerate clinical research

Annotating regions of interest in medical images, a process known as segmentation, is often one of the first steps clinical ...
IEEE

MCGC-Net: Multi-scale Controllable Graph Convolutional Network on Music Emotion Recognition

Abstract: This paper introduces a novel dynamic graph learning approach for frequency graphs, underpinned by a suite of baseline methodologies and the Multi-scale Controllable Graph Convolutional ...
C&EN

Development of Coarse-Grained Lipid Force Fields Based on a Graph Neural Network

Department of Materials Science and Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong China Department of Physics, City University of Hong Kong, Kowloon 999077, Hong Kong China ...
GitHub

SegDINO: An Efficient Design for Medical and Natural Image Segmentation with DINO-V3

We propose SegDINO, an efficient image segmentation framework that couples a frozen DINOv3 backbone with a lightweight MLP decoder, achieving state-of-the-art performance on both medical and natural ...