AIMC Topic: Neural Networks, Computer

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Contour Transformer Network for One-Shot Segmentation of Anatomical Structures.

IEEE transactions on medical imaging
Accurate segmentation of anatomical structures is vital for medical image analysis. The state-of-the-art accuracy is typically achieved by supervised learning methods, where gathering the requisite expert-labeled image annotations in a scalable manne...

Bidirectional Mapping-Based Domain Adaptation for Nucleus Detection in Cross-Modality Microscopy Images.

IEEE transactions on medical imaging
Cell or nucleus detection is a fundamental task in microscopy image analysis and has recently achieved state-of-the-art performance by using deep neural networks. However, training supervised deep models such as convolutional neural networks (CNNs) u...

Numerical Study of the Environmental and Economic System through the Computational Heuristic Based on Artificial Neural Networks.

Sensors (Basel, Switzerland)
In this study, the numerical computation heuristic of the environmental and economic system using the artificial neural networks (ANNs) structure together with the capabilities of the heuristic global search genetic algorithm (GA) and the quick local...

Confidence-Calibrated Human Activity Recognition.

Sensors (Basel, Switzerland)
Wearable sensors are widely used in activity recognition (AR) tasks with broad applicability in health and well-being, sports, geriatric care, etc. Deep learning (DL) has been at the forefront of progress in activity classification with wearable sens...

Pancreatic Cancer Survival Prediction: A Survey of the State-of-the-Art.

Computational and mathematical methods in medicine
Cancer early detection increases the chances of survival. Some cancer types, like pancreatic cancer, are challenging to diagnose or detect early, and the stages have a fast progression rate. This paper presents the state-of-the-art techniques used in...

An Unsupervised Deep Feature Learning Model Based on Parallel Convolutional Autoencoder for Intelligent Fault Diagnosis of Main Reducer.

Computational intelligence and neuroscience
Traditional diagnostic framework consists of three parts: data acquisition, feature generation, and fault classification. However, manual feature extraction utilized signal processing technologies heavily depending on subjectivity and prior knowledge...

Cognitive Matching of Design Subjects in Product Form Evolutionary Design.

Computational intelligence and neuroscience
In actual product development, the cognitive differences between users and designers make it difficult for the designed products to be recognized by users. To reduce the cognitive differences between these two design subjects, this paper proposes a m...

Potential Fault Diagnosis Method and Classification Accuracy Detection of IGBT Device Based on Improved Single Hidden Layer Feedforward Neural Network.

Computational intelligence and neuroscience
Insulated Gate Bipolar Transistor (IGBT) is a high-power switch in the field of power electronics. Its reliability is closely related to system stability. Once failure occurs, it may cause irreparable loss. Therefore, potential fault diagnosis method...

Deep learning-based endoscopic anatomy classification: an accelerated approach for data preparation and model validation.

Surgical endoscopy
BACKGROUND: Photodocumentation during endoscopy procedures is one of the indicators for endoscopy performance quality; however, this indicator is difficult to measure and audit in the endoscopy unit. Emerging artificial intelligence technology may so...

Prediction of Reaction Yield for Buchwald-Hartwig Cross-coupling Reactions Using Deep Learning.

Molecular informatics
Chemical reaction yield is one of the most important factors for determining reaction conditions. Recently, several machine learning-based prediction models using high-throughput experiment (HTE) data sets were reported for the prediction of reaction...