AIMC Topic: Neural Networks, Computer

Clear Filters Showing 10341 to 10350 of 31376 articles

Domain generalization improves end-to-end object detection for real-time surgical tool detection.

International journal of computer assisted radiology and surgery
PURPOSE: Computer assistance for endoscopic surgery depends on knowledge about the contents in an endoscopic scene. An important step of analysing the video contents is real-time surgical tool detection. Most methods for tool detection nevertheless d...

On joint parameterizations of linear and nonlinear functionals in neural networks.

Neural networks : the official journal of the International Neural Network Society
The paper proposes a new class of nonlinear operators and a dual learning paradigm where optimization jointly concerns both linear convolutional weights and the parameters of these nonlinear operators. The nonlinear class proposed to perform a rich f...

Deep learning-based method for automatic resolution of gas chromatography-mass spectrometry data from complex samples.

Journal of chromatography. A
Modern gas chromatography-mass spectrometry (GC-MS) is the workhorse for the high-throughput profiling of volatile compounds in complex samples. It can produce a considerable amount of two-dimensional data, and automatic methods are required to disti...

Mateverse, the Future Materials Science Computation Platform Based on Metaverse.

The journal of physical chemistry letters
Currently, computational materials science involves human-computer interaction through coding in software or neural networks. There is still no direct way for human intelligence endorsement. The digitalization of human intelligence should be the ulti...

Graph Convolution Based Cross-Network Multiscale Feature Fusion for Deep Vessel Segmentation.

IEEE transactions on medical imaging
Vessel segmentation is widely used to help with vascular disease diagnosis. Vessels reconstructed using existing methods are often not sufficiently accurate to meet clinical use standards. This is because 3D vessel structures are highly complicated a...

3D Soma Detection in Large-Scale Whole Brain Images via a Two-Stage Neural Network.

IEEE transactions on medical imaging
3D soma detection in whole brain images is a critical step for neuron reconstruction. However, existing soma detection methods are not suitable for whole mouse brain images with large amounts of data and complex structure. In this paper, we propose a...

Toward Multicenter Skin Lesion Classification Using Deep Neural Network With Adaptively Weighted Balance Loss.

IEEE transactions on medical imaging
Recently, deep neural network-based methods have shown promising advantages in accurately recognizing skin lesions from dermoscopic images. However, most existing works focus more on improving the network framework for better feature representation b...

TNN: Tree Neural Network for Airway Anatomical Labeling.

IEEE transactions on medical imaging
Detailed anatomical labeling of bronchial trees extracted from CT images can be used as fine-grained maps for intra-operative navigation. To cater to the sparse distribution of airway voxels and large class imbalance in 3D image space, a graph-neural...

A Flexible Memristor Model With Electronic Resistive Switching Memory Behavior and Its Application in Spiking Neural Network.

IEEE transactions on nanobioscience
Memristive technologies are attractive due to their non-volatility, high-density, low-power and compatibility with CMOS. For memristive devices, a model corresponding to practical behavioral characteristics is highly favorable for the realization of ...

Asynchronous Spiking Neural P Systems With Rules Working in the Rule Synchronization Mode.

IEEE transactions on nanobioscience
Asynchronous spiking neural P systems with rules on synapses (ARSSN P systems) are a class of computation models, where spiking rules are placed on synapses. In this work, we investigate the computation power of ARSSN P systems working in the rule sy...