Researchers developed multiple architectures, including U-Net 2D and 3D CNNs, as well as a Vision Transformer (ViT), to ...

Deep Learning has been widely used for medical image segmentation and a large number of papers have ... In Federated Learning, each client trains its own model using local data, and only the model ...

This project constructed a deep learning-based medical image semantic segmentation system for automatic recognition and extraction of clinical wound regions. Implementation based on TensorFlow 2.x. It ...

Kaizen rethinks cell segmentation by mimicking brain predictions. Using an iterative machine-learning approach to refine boundaries in crowded microscopy images, it enhances accuracy in tissue studies ...

It took ChatGPT Deep Research minutes to reverse-engineer my full GitHub repo, when I'd need days. Here's why this is a big ...

FLUX.1 Kontext from Black Forest Labs aims to let users edit images multiple times through both text and reference images without losing speed.

Additionally, the model’s hallucination rate has been reduced, contributing to more reliable and consistent output.

Leveraging the inherent continuity of slice data in CT, where lesion shapes and locations exhibit systematic and progressive changes, we propose a deep reinforcement learning (DRL) driven weakly ...

Master artificial intelligence in 2025 with this comprehensive guide. Explore AI fundamentals, machine learning, deep ...

Deep learning ... the hyperspectral image, and a classification probability map of the same spatial size as the input image is output. To obtain multi-level contextual information, three main methods ...