Convolutional neural networks (CNNs) are a type of deep model that can act directly on the raw inputs. However, such models are currently limited to handling 2D inputs. In this paper, we develop a ...

Official implementation of the paper "Feature Visualization in 3D Convolutional Neural Networks", which can disentangle texture and motion preferences of a target 3D conv kernel with a data-driven ...

A 3D-CNN is employed to automatically learn discriminative spatiotemporal features from high resolution 3D medical images. By fine tuning the model parameters using Bayesian Optimization, tool ...

The diagnostic pen is designed with mass production in mind. All its parts — from the 3D-printed casing to the replaceable ...

They are developing a Quantum Convolutional Neural Network (QCNN) architecture to enhance the performance of traditional computer vision tasks using quantum mechanics principles. The Quantum ...

Researchers developed multiple architectures, including U-Net 2D and 3D CNNs, as well as a Vision Transformer (ViT), to ...

As Innatera’s first mass-market neuromorphic MCU, Pulsar delivers intelligence at the edge by emulating the brain’s neural ...

In this paper, we innovatively propose a multi-view (coronal and transverse) attention network for semi-supervised 3D cardiac image segmentation ... We improved the VNet by adding a convolutional ...

Current deepfake detection systems, many of which were trained on first- to third-generation benchmarks, are struggling with ...