Medical image classification with convolutional neural network

By Qing Li, Weidong (Tom) Cai, Xiaogang Wang, Yun Zhou, D. Feng, Mei Chen. International Conference on Control, Automation, Robotics and Vision, 2014

740 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/701ec23ea4bde6a1c0a9817b803aacd9c8548b75

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In Artificial Intelligence

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Deep convolutional neural network based medical image classification for disease diagnosis (2019, methodology): “[28] presented a customized CNN with shallow ConvLayer to classify image patches of lung disease.”
Deep learning in bioinformatics (2016, background): “Biomedical imaging magnetic resonance image (MRI) radiographic image positron emission tomography (PET) histopathology image volumetric electron microscopy image retinal image in situ hybridization (ISH) image Anomaly classification [122-132] gene expression pattern cancer Alzheimer's disease schizo”
Uncertainty Sets for Image Classifiers using Conformal Prediction (2020, background): “This is not a casual hypothetical; such classiﬁers are already used in medical settings (e.g., Razzak et al., 2018; Lundervold & Lundervold, 2019; Li et al., 2014).”
A review of the-state-of-the-art in data-driven approaches for building energy prediction (2020, background): “CNN is well-known in the field of visual imagery analysis, such as image recognition [81], image classification[82], medical image analysis [83] and natural language processing [84].”
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning (2016, methodology): “In this work, we sample image patches from the slice using the ROIs for the ILD provided in the dataset, in order to be consistent with previous methods in patch-level [38] , [39] , [54] and slice-level classification [40] .”
Deep Learning on Image Denoising: An overview (2019, neutral): “Since 2014, deep networks have been widely used in real-world image applications, such as facial recognition [86] and medical diagnosis [124].”
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network (2020, background): “eed for a huge annotated dataset for training; therefore many researchers tend to apply this strategy especially with medical imaging. Transfer learning can be accomplished with three major scenarios [16]: a) \shallow tuning", which adapts only the last classification layer to cope with the new”
Complex-Valued Convolutional Neural Network and Its Application in Polarimetric SAR Image Classification (2017, background): “The aim of the learning procedure is to find sets of filters w that can extract discriminative features for image classification [23].”
Focus: Querying Large Video Datasets with Low Latency and Low Cost (2018, background): “, [57, 72]), information decoding in biomedical signal recordings (e.”
A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects (2020, methodology): “[123] designed a custom CNN with shallow convolution layers to the classification of interstitial lung disease.”
Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network (2016, methodology): “In [28], the authors designed a CNN with one convolutional layer and three dense layers and trained it from scratch.”
Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation (2015, background): “Additionally, recent work has shown how the implementation of ConvNets can substantially improve the performance of state-of-the-art CADe systems [19]–[22].”
Deep Learning and Convolutional Neural Networks for Medical Image Computing (2017, methodology): “This data set was downloaded via the links provided in [52].”
Convolutional neural networks in medical image understanding: a survey (2021, background): “Medical anomalies include glaucoma, diabetic retinopathy, tumors [34], interstitial lung diseases [44], heart diseases and tuberculosis.”
UNCERTAINTY MODELING AND INTERPRETABILITY IN CONVOLUTIONAL NEURAL NETWORKS FOR POLYP SEGMENTATION (2018, background): “The last couple of years have seen several works on automatic procedures based on Deep Convolutional Neural Networks (CNN) for health domain tasks such as interstitial lung disease classification [5], cell detection [6], estimation of cardiothoracic ratio [7], and polyp detection [8].”