AIMC Topic: Image Processing, Computer-Assisted

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Artificial Intelligence in Health Care: Current Applications and Issues.

Journal of Korean medical science
In recent years, artificial intelligence (AI) technologies have greatly advanced and become a reality in many areas of our daily lives. In the health care field, numerous efforts are being made to implement the AI technology for practical medical tre...

Low Rank Regularization: A review.

Neural networks : the official journal of the International Neural Network Society
Low Rank Regularization (LRR), in essence, involves introducing a low rank or approximately low rank assumption to target we aim to learn, which has achieved great success in many data analysis tasks. Over the last decade, much progress has been made...

A novel extended Kalman filter with support vector machine based method for the automatic diagnosis and segmentation of brain tumors.

Computer methods and programs in biomedicine
BACKGROUND: Brain tumors are life-threatening, and their early detection is crucial for improving survival rates. Conventionally, brain tumors are detected by radiologists based on their clinical experience. However, this process is inefficient. This...

Roto-translation equivariant convolutional networks: Application to histopathology image analysis.

Medical image analysis
Rotation-invariance is a desired property of machine-learning models for medical image analysis and in particular for computational pathology applications. We propose a framework to encode the geometric structure of the special Euclidean motion group...

Intraprostatic Tumor Segmentation on PSMA PET Images in Patients with Primary Prostate Cancer with a Convolutional Neural Network.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Accurate delineation of the intraprostatic gross tumor volume (GTV) is a prerequisite for treatment approaches in patients with primary prostate cancer (PCa). Prostate-specific membrane antigen PET (PSMA PET) may outperform MRI in GTV detection. Howe...

Deep-learning-based direct inversion for material decomposition.

Medical physics
PURPOSE: To develop a convolutional neural network (CNN) that can directly estimate material density distribution from multi-energy computed tomography (CT) images without performing conventional material decomposition.

Comparison of manual and machine learning image processing approaches to determine fungiform papillae on the tongue.

Scientific reports
Human taste perception is associated with the papillae on the tongue as they contain a large proportion of chemoreceptors for basic tastes and other chemosensation. Especially the density of fungiform papillae (FP) is considered as an index for respo...

Artificial intelligence that determines the clinical significance of capsule endoscopy images can increase the efficiency of reading.

PloS one
Artificial intelligence (AI), which has demonstrated outstanding achievements in image recognition, can be useful for the tedious capsule endoscopy (CE) reading. We aimed to develop a practical AI-based method that can identify various types of lesio...

Single image super-resolution via Image Quality Assessment-Guided Deep Learning Network.

PloS one
In recent years, deep learning (DL) networks have been widely used in super-resolution (SR) and exhibit improved performance. In this paper, an image quality assessment (IQA)-guided single image super-resolution (SISR) method is proposed in DL archit...

A deep learning framework for pancreas segmentation with multi-atlas registration and 3D level-set.

Medical image analysis
In this paper, we propose and validate a deep learning framework that incorporates both multi-atlas registration and level-set for segmenting pancreas from CT volume images. The proposed segmentation pipeline consists of three stages, namely coarse, ...