AIMC Topic: Algorithms

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A Calibrated Multiexit Neural Network for Detecting Urothelial Cancer Cells.

Computational and mathematical methods in medicine
Deep convolutional networks have become a powerful tool for medical imaging diagnostic. In pathology, most efforts have been focused in the subfield of histology, while cytopathology (which studies diagnostic tools at the cellular level) remains unde...

Multi-Nyström Method Based on Multiple Kernel Learning for Large Scale Imbalanced Classification.

Computational intelligence and neuroscience
Extensions of kernel methods for the class imbalance problems have been extensively studied. Although they work well in coping with nonlinear problems, the high computation and memory costs severely limit their application to real-world imbalanced ta...

Adaptive neural network asymptotic tracking control for nonstrict feedback stochastic nonlinear systems.

Neural networks : the official journal of the International Neural Network Society
The adaptive neural network asymptotic tracking control issue of nonstrict feedback stochastic nonlinear systems is studied in our article by adopting backstepping algorithm. Compared with the existing research, the hypothesis about unknown virtual c...

Finite time convergence of pinning synchronization with a single nonlinear controller.

Neural networks : the official journal of the International Neural Network Society
In this paper, we discuss distributive synchronization of complex networks in finite time, with a single nonlinear pinning controller. The results apply to heterogeneous dynamic networks, too. Different from many models, which assume the coupling mat...

How to handle noisy labels for robust learning from uncertainty.

Neural networks : the official journal of the International Neural Network Society
Most deep neural networks (DNNs) are trained with large amounts of noisy labels when they are applied. As DNNs have the high capacity to fit any noisy labels, it is known to be difficult to train DNNs robustly with noisy labels. These noisy labels ca...

HyAdamC: A New Adam-Based Hybrid Optimization Algorithm for Convolution Neural Networks.

Sensors (Basel, Switzerland)
As the performance of devices that conduct large-scale computations has been rapidly improved, various deep learning models have been successfully utilized in various applications. Particularly, convolution neural networks (CNN) have shown remarkable...

Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on international classification of functioning, disability and health concept.

Topics in stroke rehabilitation
INTRODUCTION: To understand the current practices in stroke evaluation, the main clinical decision support system and artificial intelligence (AI) technologies need to be understood to assist the therapist in obtaining better insights about impairmen...

Machine learning for surgical time prediction.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Operating Rooms (ORs) are among the most expensive services in hospitals. A challenge to optimize the OR efficiency is to improve the surgery scheduling task, which requires the estimation of surgical time duration. Surgeons...

Effect of deep learning image reconstruction in the prediction of resectability of pancreatic cancer: Diagnostic performance and reader confidence.

European journal of radiology
OBJECTIVE: To assess the diagnostic performance and reader confidence in determining the resectability of pancreatic cancer at computed tomography (CT) using a new deep learning image reconstruction (DLIR) algorithm.

Diagnostic Accuracy and Failure Mode Analysis of a Deep Learning Algorithm for the Detection of Cervical Spine Fractures.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Artificial intelligence decision support systems are a rapidly growing class of tools to help manage ever-increasing imaging volumes. The aim of this study was to evaluate the performance of an artificial intelligence decision...