An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification

By Ashnil Kumar, Jinman Kim, David Lyndon, Michael J. Fulham, D. Feng. 2017

473 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine (2020, methodology): “Typical deep learning techniques used in medical imaging include convolutional neural networks for image classification [6], recurrent neural networks for reporting tasks [7], and a U-net for image segmentation [8].”
Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI (2018, methodology): “An ensemble of fine-tuned CNN classifiers was shown to outperform traditional CNNs in predicting the radiological image modality on a test set of 4166 images.(32) A comparison of approaches using deep features and transfer learning with fine-tuning has been shown useful for identifying radiogenomic ”
Recent advances and clinical applications of deep learning in medical image analysis (2021, methodology): “…has become a cornerstone for image classification tasks (de Bruijne, 2016), which has proven effective for a variety of modalities, including CT (Kumar et al., 2017; Shin et al., 2016), mammography (Huynh et al., 2016), MRI (Kumar et al., 2017), ultrasonography (Liu et al., 2017; Droste et al.,…”
FedBoost: Communication-Efficient Algorithms for Federated Learning (2020, unknown): “Ensemble methods often significantly improve performance in practice in a variety of tasks (Dietterich, 2000), including image classification (Kumar et al., 2016) and language models (Jozefowicz et al., 2016).”
Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks (2017, methodology): “In order to achieve more precise details of pixel prediction, we fine-tuned a stride-8 FCN architecture (FCN-8s) on the ISBI 2016 Skin Lesion Challenge training dataset (more details about the dataset can be found in Section III); Data augmentation techniques including random crops and flips were us”
Deep learning in medical imaging and radiation therapy. (2018, background): “” 18 Systems using transfer learning often outperform the state-of-the-art methods based on traditional handcrafted features that were developed over many years with a great deal of expertise.”
Ensemble of Deep Convolutional Neural Networks for Automatic Pavement Crack Detection and Measurement (2020, methodology): “• Ensemble Learning: An ensemble network was proposed to perform medical image classification [31].”
Deep learning for image classification on very small datasets using transfer learning (2019, background): “However, they explore the transfer learning in person re-identification in large datasets like [27], [28], and [29].”
Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM (2021, background): “They can recognize the condition based on the standard images through image processing techniques, including image transformation, equalization, enhancement, edge detection, and segmentation [19–21].”