AIMC Topic: Image Processing, Computer-Assisted

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Dual-Path Deep Fusion Network for Face Image Hallucination.

IEEE transactions on neural networks and learning systems
Along with the performance improvement of deep-learning-based face hallucination methods, various face priors (facial shape, facial landmark heatmaps, or parsing maps) have been used to describe holistic and partial facial features, making the cost o...

Simple and Effective: Spatial Rescaling for Person Reidentification.

IEEE transactions on neural networks and learning systems
Global average pooling (GAP) allows convolutional neural networks (CNNs) to localize discriminative information for recognition using only image-level labels. While GAP helps CNNs to attend to the most discriminative features of an object, e.g., head...

Deep RED Unfolding Network for Image Restoration.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
The deep unfolding network (DUN) provides an efficient framework for image restoration. It consists of a regularization module and a data fitting module. In existing DUN models, it is common to directly use a deep convolution neural network (DCNN) as...

Two-Step Registration on Multi-Modal Retinal Images via Deep Neural Networks.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Multi-modal retinal image registration plays an important role in the ophthalmological diagnosis process. The conventional methods lack robustness in aligning multi-modal images of various imaging qualities. Deep-learning methods have not been widely...

Verte-Box: A Novel Convolutional Neural Network for Fully Automatic Segmentation of Vertebrae in CT Image.

Tomography (Ann Arbor, Mich.)
Due to the complex shape of the vertebrae and the background containing a lot of interference information, it is difficult to accurately segment the vertebrae from the computed tomography (CT) volume by manual segmentation. This paper proposes a conv...

An artificial intelligence-accelerated 2-minute multi-shot echo planar imaging protocol for comprehensive high-quality clinical brain imaging.

Magnetic resonance in medicine
PURPOSE: We introduce and validate an artificial intelligence (AI)-accelerated multi-shot echo-planar imaging (msEPI)-based method that provides T1w, T2w, , T2-FLAIR, and DWI images with high SNR, high tissue contrast, low specific absorption rates ...

Deep transfer learning based model for colorectal cancer histopathology segmentation: A comparative study of deep pre-trained models.

International journal of medical informatics
Colorectal cancer is one of the leading causes of cancer-related death, worldwide. Early detection of suspicious tissues can significantly improve the survival rate. In this study, the performance of a wide variety of deep learning-based architecture...

Use of the deep learning approach to measure alveolar bone level.

Journal of clinical periodontology
AIM: The goal was to use a deep convolutional neural network to measure the radiographic alveolar bone level to aid periodontal diagnosis.

A Two-Stage Approach to Important Area Detection in Gathering Place Using a Novel Multi-Input Attention Network.

Sensors (Basel, Switzerland)
An important area in a gathering place is a region attracting the constant attention of people and has evident visual features, such as a flexible stage or an open-air show. Finding such areas can help security supervisors locate the abnormal regions...

Optical coherence tomography for identification of malignant pulmonary nodules based on random forest machine learning algorithm.

PloS one
OBJECTIVE: To explore the feasibility of using random forest (RF) machine learning algorithm in assessing normal and malignant peripheral pulmonary nodules based on in vivo endobronchial optical coherence tomography (EB-OCT).