We develop a new optimization framework, genetic-based evolutionary strategy, to optimize the convolutional neural networks architecture for the Chest X-Ray classification task.
We develop a image editor toolkit based on purely numpy that supports basic image manipulations.
We present MTSegFormer, a learning framework for 2D brain tumor segmentation using latent transformer through the Multi-task learning paradigm.
In this paper, we compared the performance of various deep models, ranging from traditional machine learning methods, CNN-based deep learning methods to the modern state-of-the-art Vision Transformer (ViT) model. The results have showen that ViTs achieved a decent performance in X-Ray classification task, and transfer learning improved ViT by a large margin.
Use recipe ingredients to categorize the cuisine through machine learning and deep learning models.