IEEE

Spectral Structure-Aware Initialization and Probability-Consistent Self-Training for Cross-Scene Hyperspectral Image Classification

Abstract: Cross-scene classification of hyperspectral images (HSIs) aims to classify target domain (TD) data using only labeled source domain (SD) data and unlabeled TD data during training. However, ...
Frontiers

Label tree semantic losses for rich multi-class medical image segmentation

Rich and accurate medical image segmentation is poised to underpin the next generation of AI-defined clinical practice by delineating critical anatomy for pre-operative planning, guiding real-time ...
IEEE

Enhancing Medical Image Classification With Context Modulated Attention and Multi-Scale Feature Fusion

Abstract: This research proposes a multi-stage feature fusion network (MSFF) for medical image classification. In view of the problems existing in medical images, such as noise, diversity, and ...
GitHub

Ideogram 4: Open image model at the forefront of design

Ideogram 4 is Ideogram's first open-weight text-to-image model. It is a state-of-the-art foundation model trained from scratch — not a fine-tune of any existing model. It introduces a new structured ...