AIMC Topic: Algorithms

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Self-Path: Self-Supervision for Classification of Pathology Images With Limited Annotations.

IEEE transactions on medical imaging
While high-resolution pathology images lend themselves well to 'data hungry' deep learning algorithms, obtaining exhaustive annotations on these images for learning is a major challenge. In this article, we propose a self-supervised convolutional neu...

Active Cell Appearance Model Induced Generative Adversarial Networks for Annotation-Efficient Cell Segmentation and Identification on Adaptive Optics Retinal Images.

IEEE transactions on medical imaging
Data annotation is a fundamental precursor for establishing large training sets to effectively apply deep learning methods to medical image analysis. For cell segmentation, obtaining high quality annotations is an expensive process that usually requi...

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...

Role of Machine Learning in Resource Allocation Strategy over Vehicular Networks: A Survey.

Sensors (Basel, Switzerland)
The increasing demand for smart vehicles with many sensing capabilities will escalate data traffic in vehicular networks. Meanwhile, available network resources are limited. The emergence of AI implementation in vehicular network resource allocation ...

Multi-Robot Preemptive Task Scheduling with Fault Recovery: A Novel Approach to Automatic Logistics of Smart Factories.

Sensors (Basel, Switzerland)
This paper presents a novel approach for Multi-Robot Task Allocation (MRTA) that introduces priority policies on preemptive task scheduling and considers dependencies between tasks, and tolerates faults. The approach is referred to as Multi-Robot Pre...

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...

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...

A static analysis approach for Android permission-based malware detection systems.

PloS one
The evolution of malware is causing mobile devices to crash with increasing frequency. Therefore, adequate security evaluations that detect Android malware are crucial. Two techniques can be used in this regard: Static analysis, which meticulously ex...

Comparison of visibility of in-stent restenosis between conventional- and ultra-high spatial resolution computed tomography: coronary arterial phantom study.

Japanese journal of radiology
PURPOSE: The purposes of this experimental study were to compare the quantitative and qualitative visibility of in-stent restenosis between conventional-resolution CT (CRCT) and ultra-high-resolution CT (U-HRCT) and to investigate the effects of the ...

Detection of malignant melanoma in H&E-stained images using deep learning techniques.

Tissue & cell
Histopathological images are widely used to diagnose diseases including skin cancer. As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of all abnormal cell nuclei an...