Arcee's U.S.-made, open source Trinity Large and 10T-checkpoint offer rare look at raw model intelligence

San Francisco-based AI lab Arcee made waves last year for being one of the only U.S. companies to train large language models (LLMs) from scratch and release them under open or partially open source ...

AI models that simulate internal debate dramatically improve accuracy on complex tasks

A new study reveals that top models like DeepSeek-R1 succeed by simulating internal debates. Here is how enterprises can harness this "society of thought" to build more robust, self-correcting agents.
Time

OpenAI’s Sora Underscores the Growing Threat of Deepfakes

Sora by OpenAI at the top of the free app rankings on the App Store. Sora by OpenAI at the top of the free app rankings on the App Store. Photo illustration by Cheng ...
Frontiers

Multi-interactive feature embedding learning for medical image segmentation

1 School of Public Health, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China 2 School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, ...
IEEE

SDCnet: A Deep Learning Network Model for Road Segmentation Based on CNN and Transformer

Abstract: Road segmentation is a key task in remote sensing semantic segmentation, and the existing deep learning methods still have the problems of insufficient fineness, difficulty in modeling ...
Scientific Research Publishing

From U-Net to Swin-Unet Transformers: The Next-Generation Advances in Brain Tumor Segmentation with Deep Learning ()

Brain tumor segmentation is a vital step in diagnosis, treatment planning, and prognosis in neuro-oncology. In recent years, deep learning approaches have revolutionized this field, evolving from the ...
News Medical

Deep learning method enhances vessel and plaque segmentation in stroke diagnosis

Stroke is the second leading cause of death globally. Ischemic stroke, strongly linked to atherosclerotic plaques, requires accurate plaque and vessel wall segmentation and quantification for ...
IEEE

A Deep Learning Multimodal Fusion-Based Method for Cell and Nucleus Segmentation

Abstract: In recent years, deep learning has been widely utilized in the fields of biomedical image segmentation and cellular image analysis. Supervised deep neural networks trained on annotated data ...
Frontiers

Brain tumor segmentation using deep learning: high performance with minimized MRI data

Purpose: Brain tumor segmentation with MRI is a challenging task, traditionally relying on manual delineation of regions-of-interest across multiple imaging sequences. However, this data-intensive ...