AIMC Topic: Algorithms

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Multistability of switched neural networks with sigmoidal activation functions under state-dependent switching.

Neural networks : the official journal of the International Neural Network Society
This paper presents theoretical results on the multistability of switched neural networks with commonly used sigmoidal activation functions under state-dependent switching. The multistability analysis with such an activation function is difficult bec...

Integrative Deep Learning for Identifying Differentially Expressed (DE) Biomarkers.

Computational and mathematical methods in medicine
As a large amount of genetic data are accumulated, an effective analytical method and a significant interpretation are required. Recently, various methods of machine learning have emerged to process genetic data. In addition, machine learning analysi...

Unsupervised 3D End-to-End Medical Image Registration With Volume Tweening Network.

IEEE journal of biomedical and health informatics
3D medical image registration is of great clinical importance. However, supervised learning methods require a large amount of accurately annotated corresponding control points (or morphing), which are very difficult to obtain. Unsupervised learning m...

A deep learning reconstruction framework for X-ray computed tomography with incomplete data.

PloS one
As a powerful imaging tool, X-ray computed tomography (CT) allows us to investigate the inner structures of specimens in a quantitative and nondestructive way. Limited by the implementation conditions, CT with incomplete projections happens quite oft...

An optimal interval type-2 fuzzy logic control based closed-loop drug administration to regulate the mean arterial blood pressure.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The main aim of this work is to present an optimal and robust controller design in order to improve the drug infusion to the automatic control of mean arterial blood pressure in conditions like critically-ill or post-operati...

Deep learning, reusable and problem-based architectures for detection of consolidation on chest X-ray images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In most patients presenting with respiratory symptoms, the findings of chest radiography play a key role in the diagnosis, management, and follow-up of the disease. Consolidation is a common term in radiology, which indicate...

Temporally downsampled cerebral CT perfusion image restoration using deep residual learning.

International journal of computer assisted radiology and surgery
PURPOSE: Acute ischemic stroke is one of the most causes of death all over the world. Onset to treatment time is critical in stroke diagnosis and treatment. Considering the time consumption and high price of MR imaging, CT perfusion (CTP) imaging is ...

Predicting the quality of surgical exposure using spatial and procedural features from laparoscopic videos.

International journal of computer assisted radiology and surgery
PURPOSE : Evaluating the quality of surgical procedures is a major concern in minimally invasive surgeries. We propose a bottom-up approach based on the study of Sleeve Gastrectomy procedures, for which we analyze what we assume to be an important in...

Deep learning predicts extreme preterm birth from electronic health records.

Journal of biomedical informatics
OBJECTIVE: Models for predicting preterm birth generally have focused on very preterm (28-32 weeks) and moderate to late preterm (32-37 weeks) settings. However, extreme preterm birth (EPB), before the 28th week of gestational age, accounts for the m...