Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

By Marios Anthimopoulos, Stergios Christodoulidis, Lukas Ebner, Andreas Christe, Stavroula G. Mougiakakou. IEEE Transactions on Medical Imaging, 2016

1,170 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/03aca587f27fda3cbdad708aa69c07fc71b691d7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. P. Heng
2. Qi-Yan Dou
3. Jing Qin
4. Hao Chen
5. Lequan Yu
6. Zhedong Zheng
7. Yi Yang
8. Adnan Qayyum

In Pulmonary and Respiratory Medicine

1. Qi-Yan Dou
2. D. Feng
3. Nikos Paragios
4. P. Heng
5. Michael J. Fulham
6. Lequan Yu
7. Hao Chen
8. Ashnil Kumar

In Radiology, Nuclear Medicine and Imaging

1. P. Heng
2. Hao Chen
3. Jing Qin
4. Qi-Yan Dou
5. Lequan Yu
6. Yi Yang
7. B. van Ginneken
8. Hayit Greenspan

Citations in the corpus

• Deep Learning for Health Informatics (2017, methodology): “Example applications of CNNs in medical imaging include the classification of interstitial lung diseases based on computed tomography (CT) images [70], the classification of tuberculosis manifestation based on X-ray images [71], the classification of neural progenitor cells from somatic cell source ”
• Fully Automated Deep Learning System for Bone Age Assessment (2017, methodology): “From using deep CNNs to detect patterns of interstitial lung disease on 2D patches of chest CTs [5] to segmenting the vascular network of the human eye on fundus photos [6], deep CNNs have proven enormously successful since they enable learning highly representative, layered, hierarchical abstractio”
• Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification (2018, background): “Recent surveys [29], [30], [31], [32] have demonstrated that deep learning technologies have been extensively applied to the field of chest X-ray image annotation [33], classification [6], [34], [8], [9], and detection (localization) [35], [36].”
• A review on deep learning in medical image analysis (2021, background): “It could include the use of unsupervised methods like clustering and density estimation, or it could be inspired by them to make effective use of unlabelled data [49, 50].”
• A Survey of Deep Learning for Scientific Discovery (2020, background): “Examples range from ophthalmology [72], radiology (2D x-rays and 3D CT scans) [258, 5, 185],”
• Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique (2016, background): “[16] focus on detecting patterns of interstitial lung diseases from 2D patches of chest CT scans.”
• Survey on Deep Neural Networks in Speech and Vision Systems (2019, background): “Advances in deep neural networks have shown tremendous potential in almost all areas of medical imaging such as ophthalmology [215], dental radiography [216], skin cancer imaging [217], brain imaging [218], cardiac imaging [219, 220], urology [221], lung imaging [222], stroke imaging [223], and so o”
• An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification (2017, background): “[25] showed that CNN architectures have higher accuracy than other methods when significant effort has been expended to acquire labels for the training data.”
• Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT (2020, background): “Attempts have also made to detect various imaging features of chest CT (14,15).”
• Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning (2017, methodology): “To deal with image data, deep convolutional neural network (DCNN) is used 28–33 .”
• Deep learning with convolutional neural network in radiology (2018, methodology): “86 by using CNN models based on chest CT images [52].”