This paper introduces a hybrid deep learning and machine learning pipeline for classifying construction and demolition waste. A dataset of 1,800 images from UAE construction sites was created, and deep features were extracted using a pre-trained Xception network. The combination of Xception features with machine learning classifiers achieved up to 99.5% accuracy, demonstrating state-of-the-art performance for debris identification.
Researchers from MBZUAI have developed XReal, a diffusion model for generating realistic chest X-ray images with precise control over anatomy and pathology location. The model utilizes an Anatomy Controller and a Pathology Controller to introduce spatial control in a pre-trained Text-to-Image Diffusion Model without fine-tuning. XReal outperforms existing X-ray diffusion models in realism, as evaluated by quantitative metrics and radiologists' ratings, and the code/weights are available.
Researchers at MBZUAI introduce FissionFusion, a hierarchical model merging approach to improve medical image analysis performance. The method uses local and global aggregation of models based on hyperparameter configurations, along with a cyclical learning rate scheduler for efficient model generation. Experiments show FissionFusion outperforms standard model souping by approximately 6% on HAM10000 and CheXpert datasets and improves OOD performance.