A QuPath extension that brings XGBoost gradient-boosted tree classification to object classification workflows. Train a model from point or area annotations across multiple project images, then run ...
Abstract: Decision trees in machine learning achieved satisfactory performance in classification. Decision trees offer the advantage of handling high-dimensional and complexly correlated data through ...
This study aimed to develop and evaluate a non-invasive XGBoost-based machine learning model using radiomic features extracted from pre-treatment CT images to differentiate grade 4 renal cell ...
This study introduces the XGBoost Multimodal Autism Predictor (XMAP), an interpretable machine learning framework designed to improve ASD classification by integrating publicly available behavioral ...
Abstract: Classification and segmentation using ultra-fine-grained datasets can be challenging due to the small nuances between adjacent classes. This problem can be exacerbated by the fact that ...
ABSTRACT: In many fields, particularly that of health, the diagnosis of diseases is a very difficult task to carry out. Therefore, early detection of diseases using artificial intelligence tools can ...
PR1, W1, T51, F58, SL4, KL3, SM11. This is not a test to crack a code. But you will see a series of letter and number combinations while engaging with the Paralympics in Paris. At the Olympics, there ...
Mangrove ecosystems, vital for biodiversity and climate change mitigation, face challenges in monitoring and conservation due to their complex species composition. A new study introduces an AI-driven ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results