☆ 4.7 Article

CM-SegNet: A deep learning-based automatic segmentation approach for medical images by combining convolution and multilayer perceptron

COMPUTERS IN BIOLOGY AND MEDICINE (2022)

Related references

Note: Only part of the references are listed.

Review Computer Science, Artificial Intelligence

A review on deep learning in medical image analysis

S. Suganyadevi et al.

Summary: Ongoing advances in AI, particularly in deep learning techniques, have been crucial in identifying, classifying, and quantifying patterns in clinical images. The rapid development of deep learning has found effective applications in various medical fields, including neurology, pathology, radiology, and more. This paper presents research on the application of deep learning in medical image processing, showcasing both fundamental information and cutting-edge approaches in this area.

INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL (2022)

Add to Collection

Article Engineering, Biomedical

Automated lung ultrasound scoring for evaluation of coronavirus disease 2019 pneumonia using two-stage cascaded deep learning model

Wenyu Xing et al.

Summary: This study proposes an automated lung ultrasound (LUS) scoring system based on a two-stage cascaded deep learning model for evaluating COVID-19 pneumonia. The experiments demonstrate that the model performs well in terms of accuracy, sensitivity, specificity, and F1-score.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2022)

Add to Collection

Article Computer Science, Artificial Intelligence

AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system

Bo Wang et al.

Summary: This paper presents the experience of building and deploying an AI system for rapid detection of COVID-19 pneumonia, which can save time for physicians and improve the performance of COVID-19 detection. The authors overcame various challenges in a interdisciplinary team and successfully deployed the system in four weeks.

APPLIED SOFT COMPUTING (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Automated ultrasound assessment of amniotic fluid index using deep learning

Hyun Cheol Cho et al.

Summary: A deep learning-based method has been developed to automatically measure antenatal amniotic fluid volume, incorporating anatomical knowledge of clinicians. Experimental results show that the proposed method provides precise amniotic fluid index measurement comparable to clinicians' results.

MEDICAL IMAGE ANALYSIS (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Lung segmentation and automatic detection of COVID-19 using radiomic features from chest CT images

Chen Zhao et al.

Summary: This paper proposes an automatic method to segment pulmonary parenchyma in chest CT images and analyze texture features for assisting radiologists in COVID-19 diagnosis. Experimental results demonstrate high accuracy and reliability in segmentation and diagnosis using sophisticated statistical models and radiomic features with high interpretability.

PATTERN RECOGNITION (2021)

Add to Collection

Article Health Care Sciences & Services

HybridCTrm: Bridging CNN and Transformer for Multimodal Brain Image Segmentation

Qixuan Sun et al.

Summary: This study proposes a hybrid multimodal segmentation method based on Transformer and CNN, showing superior performance over traditional fully CNN methods in most evaluation metrics. The study also analyzes the influence of Transformer depth on performance, visualizes the results, and explores how the hybrid methods improve segmentations.

JOURNAL OF HEALTHCARE ENGINEERING (2021)

Add to Collection

Review Biology

Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review

Afshin Shoeibi et al.

Summary: Multiple Sclerosis (MS) is a brain disease that causes various issues and is diagnosed using MRI, which can be time-consuming and prone to errors. Utilizing a computer aided diagnosis system (CADS) based on artificial intelligence involves conventional machine learning and deep learning methods for MS diagnosis.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

Add to Collection

Article Computer Science, Information Systems

A novel deep learning framework for lung nodule detection in 3d CT images

Reza Majidpourkhoei et al.

Summary: The paper presents a framework for identifying pulmonary nodules in lung CT images and uses a convolutional neural network (CNN) approach to automatically extract features and classify suspicious regions. The proposed system achieves high accuracy and sensitivity, making it suitable for real-time medical image analysis after training and validation.

MULTIMEDIA TOOLS AND APPLICATIONS (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

MANet: A two-stage deep learning method for classification of COVID-19 from Chest X-ray images

Yujia Xu et al.

Summary: The study aims to distinguish COVID-19 positive cases from the other four classes using CXR images by proposing a two-stage classification method MANet. The experimental results demonstrate that classification models with MA have higher accuracy, stable training process, interpretability, and generalization ability. Among the evaluated models, ResNet50 with MA achieves the highest average test accuracy and provides clinicians with reliable diagnosis assistance.

NEUROCOMPUTING (2021)

Add to Collection

Article Computer Science, Information Systems

Integrating Lung Parenchyma Segmentation and Nodule Detection With Deep Multi-Task Learning

Weihua Liu et al.

Summary: The paper proposes a deep multi-task learning approach to integrate lung parenchyma segmentation and lung nodule detection tasks, achieving better performance in lung nodule detection. It uses lung parenchyma segmentation as an attention module and introduces a novel pyramid dilated convolution block to tackle the gridding problem of dilated convolution. Experimental results demonstrate significant improvements compared to state-of-the-art counterparts.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2021)

Add to Collection

Review Physiology

Recent Advances in Fibrosis and Scar Segmentation From Cardiac MRI: A State-of-the-Art Review and Future Perspectives

Yinzhe Wu et al.

Summary: Segmentation of cardiac fibrosis and scars is critical for clinical diagnosis and treatment guidance, with advanced methods like deep learning showing more efficient and accurate results.

FRONTIERS IN PHYSIOLOGY (2021)

Add to Collection

Article Ophthalmology

Automated diagnosis and quantitative analysis of plus disease in retinopathy of prematurity based on deep convolutional neural networks

Jianbo Mao et al.

ACTA OPHTHALMOLOGICA (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation

Nabil Ibtehaz et al.

NEURAL NETWORKS (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

SDCT-AuxNetθ: DCT augmented stain deconvolutional CNN with auxiliary classifier for cancer diagnosis

Shiv Gehlot et al.

MEDICAL IMAGE ANALYSIS (2020)

Add to Collection

Article Computer Science, Interdisciplinary Applications

TeTrIS: Template Transformer Networks for Image Segmentation With Shape Priors

Matthew Chung Hai Lee et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2019)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks

Mohammed A. Al-Masni et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2018)

Add to Collection

Article Computer Science, Interdisciplinary Applications

DRINet for Medical Image Segmentation

Liang Chen et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2018)

Add to Collection

Article Computer Science, Information Systems

A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification

Mugahed A. Al-antari et al.

INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS (2018)

Add to Collection

Article Multidisciplinary Sciences

Data Descriptor: Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features

Spyridon Bakas et al.

SCIENTIFIC DATA (2017)

Add to Collection

Article Computer Science, Interdisciplinary Applications

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Bjoern H. Menze et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2015)

Add to Collection

Article Radiology, Nuclear Medicine & Medical Imaging

Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool

Abdel Aziz Taha et al.

BMC MEDICAL IMAGING (2015)

Add to Collection

Article Neurosciences

Performance measure characterization for evaluating neuroimage segmentation algorithms

Herng-Hua Chang et al.

NEUROIMAGE (2009)

Add to Collection

Article Computer Science, Artificial Intelligence

A hybrid framework for 3D medical image segmentation

T Chen et al.

MEDICAL IMAGE ANALYSIS (2005)

Add to Collection

© Peeref 2019-2024. All rights reserved.