Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification

By Wei Shen, Mu Zhou, Feng Yang, Dongdong Yu, Di Dong, Caiyun Yang, Yali Zang, Jie Tian. Pattern Recognition, 2017

615 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/c6ddfcc5a18d30cac8dd1b1e66e073bd5b076a3e

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In Pulmonary and Respiratory Medicine

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT (2019, methodology): “[30], and Dhara et al.”
Applications of Deep Learning and Reinforcement Learning to Biological Data (2017, methodology): “CNN was also used extensively: on CT scans to detect– anatomical structure [136], sclerotic metastases of spine along with colonic polyps and lymph nodes (LN) [137], thoracoabdominal LN and interstitial lung disease (ILD) [139], pulmonary nodules [138,140,141]; on (f)MRI and diffusion tensor images ”
Cancer Diagnosis Using Deep Learning: A Bibliographic Review (2019, methodology): “A multi-variant Convolutional neural network (Mc-CNN) was constructed [57].”
Deep Feature Learning for Medical Image Analysis with Convolutional Autoencoder Neural Network (2017, methodology): “[22], the proposed scheme is significantly improved that the unsupervised autoencoder and CNN are collaborative to extract the features from the image.”
The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges (2019, result): “proposed a deep learning model based on CT images and achieved better prediction results for malignant lung nodule compared with previous methods [133].”
A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework (2021, methodology): “described a Multi-crop CNN (MC-CNN)-based nodule malignancy classification method [20].”
DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification (2018, methodology): “From the table 1, our deep 3D DPN achieves better performance than those of Multi-scale CNN [26], Vanilla 3D CNN [33] and Multi-crop CNN [27], because of the strong power of 3D structure and deep dual path network.”