Abstract: Chest radiography remains a frontline imaging modality for rapid, low-cost screening and monitoring of pulmonary disease worldwide. This paper presents AC-CXR, a unified deep learning ...

@InProceedings{Saijo2024_TFLoco, author = {Saijo, Kohei and Wichern, Gordon and Germain, Fran\c{c}ois G. and Pan, Zexu and {Le Roux}, Jonathan}, title = {TF-Locoformer: Transformer with Local Modeling ...

Medical image segmentation is vital for accurate diagnosis. While U-Net-based models are effective, they struggle to capture long-range dependencies in complex anatomy. We propose GH-UNet, a ...

The robustness and generalization of medical image segmentation models are being challenged by the differences between different disease types, different image types, and different cases.Deep learning ...

The world of artificial intelligence (AI) is rapidly evolving, and AI is increasingly enabling applications that were previously unattainable or very difficult to implement. A subsequent article, ...

Convolution filters are a fundamental building block in image processing and computer vision. They are used to extract specific features from an image by applying a small matrix of numbers, called the ...

Abstract: Pedestrian detection relying on deep convolution neural networks has made significant progress. Though promising results have been achieved on standard pedestrians, the performance on ...

If you find FDSSC useful in your research, please consider citing. We use Tensorflow-gpu as our computing backend, and you can also use theano as computing backend ...

Five ILSVRC-2010 test images in the first column. Remaining columns show the training images that produce feature vectors in the last hidden layer with the smallest Euclidean distance from the feature ...

The receptive field is defined as the region in the input space that a particular CNN’s feature is looking at (i.e. be affected by). For convolutional neural network, the number of output features in ...