AIMC Topic: Image Processing, Computer-Assisted

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Cross-Scale Residual Network: A General Framework for Image Super-Resolution, Denoising, and Deblocking.

IEEE transactions on cybernetics
In general, image restoration involves mapping from low-quality images to their high-quality counterparts. Such optimal mapping is usually nonlinear and learnable by machine learning. Recently, deep convolutional neural networks have proven promising...

How to Analyze the Neurodynamic Characteristics of Pulse-Coupled Neural Networks? A Theoretical Analysis and Case Study of Intersecting Cortical Model.

IEEE transactions on cybernetics
The intersecting cortical model (ICM), initially designed for image processing, is a special case of the biologically inspired pulse-coupled neural-network (PCNN) models. Although the ICM has been widely used, few studies concern the internal activit...

Leukocyte Segmentation Method Based on Adaptive Retinex Correction and U-Net.

Computational and mathematical methods in medicine
To address the issues of uneven illumination and inconspicuous leukocyte properties in the gathered cell pictures, a leukocyte segmentation method based on adaptive retinex correction and U-net was proposed. The procedure begins by processing a perip...

AdjointBackMap: Reconstructing effective decision hypersurfaces from CNN layers using adjoint operators.

Neural networks : the official journal of the International Neural Network Society
There are several methods in the exploration of Convolutional Neural Networks' (CNNs') inner workings. However, in general, finding the inverse of the function performed by CNNs as a whole is an ill-posed problem. In this paper, we propose a method b...

Segmentation of laser induced retinal lesions using deep learning (December 2021).

Lasers in surgery and medicine
OBJECTIVE: Detection of retinal laser lesions is necessary in both the evaluation of the extent of damage from high power laser sources, and in validating treatments involving the placement of laser lesions. However, such lesions are difficult to det...

Unsupervised domain adaptation for clinician pose estimation and instance segmentation in the operating room.

Medical image analysis
The fine-grained localization of clinicians in the operating room (OR) is a key component to design the new generation of OR support systems. Computer vision models for person pixel-based segmentation and body-keypoints detection are needed to better...

Deep Learning-Based Automatic Detection of Brain Metastases in Heterogenous Multi-Institutional Magnetic Resonance Imaging Sets: An Exploratory Analysis of NRG-CC001.

International journal of radiation oncology, biology, physics
PURPOSE: Deep learning-based algorithms have been shown to be able to automatically detect and segment brain metastases (BMs) in magnetic resonance imaging, mostly based on single-institutional data sets. This work aimed to investigate the use of dee...

Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Accurate and rapid measurement of the MRI volume of meningiomas is essential in clinical practice to determine the growth rate of the tumor. Imperfect automation and disappointing performance for small meningiomas of previous automated vo...

MemBrain: A deep learning-aided pipeline for detection of membrane proteins in Cryo-electron tomograms.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Cryo-electron tomography (cryo-ET) is an imaging technique that enables 3D visualization of the native cellular environment at sub-nanometer resolution, providing unpreceded insights into the molecular organization of cells....

A multi-level feature-fusion-based approach to breast histopathological image classification.

Biomedical physics & engineering express
Previously, convolutional neural networks mostly used deep semantic feature information obtained from several convolutions for image classification. Such deep semantic features have a larger receptive field, and the features extracted are more effect...