Frontiers

Advanced AI for missing modality learning and uncertainty‑aware medical image analysis

Modern medical imaging increasingly relies on artificial intelligence to support detection, diagnosis, and prognostic ...
News-Medical.Net

Advancing 3D imaging, automation, and phenotypic discovery with AI-driven high-content screening

In this interview, AZoLife Sciences speaks with Boyd Butler, a microscopy and high-content screening expert at Molecular ...
IEEE

Region Uncertainty Estimation for Medical Image Segmentation With Noisy Labels

Abstract: The success of deep learning in 3D medical image segmentation hinges on training with a large dataset of fully annotated 3D volumes, which are difficult and time-consuming to acquire.
GitHub

In-field 3D Reconstruction, Instance Segmentation and Phenotyping of Wheat Heads with Gaussian Splatting

Data used for Wheat3DGS and the baseline results for each plot (from 461 to 467) are structured as follows: Note that resolution is set to 1 to prevent image downscaling, and the training/test split ...
IEEE

Retrieval-Augmented Few-Shot Medical Image Segmentation With Foundation Models

Abstract: Medical image segmentation is crucial for clinical decision-making, but the scarcity of annotated data presents significant challenges. Few-shot segmentation (FSS) methods show promise but ...
GitHub

iodriller/SegCraft-Semantic-Segmentation

For NVIDIA GPUs, install the CUDA-enabled PyTorch wheel that matches your system from the PyTorch install page, then run: segcraft validate segcraft predict --preset cityscapes_video --local ...