Medical image classification with convolutional neural network
Abstract
Image patch classification is an important task in many different medical imaging applications. In this work, we have designed a customized Convolutional Neural Networks (CNN) with shallow convolution layer to classify lung image patches with interstitial lung disease (ILD). While many feature descriptors have been proposed over the past years, they can be quite complicated and domain-specific. Our customized CNN framework can, on the other hand, automatically and efficiently learn the intrinsic image features from lung image patches that are most suitable for the classification purpose. The same architecture can be generalized to perform other medical image or texture classification tasks.
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Both [38] and [39] use leave-one-patient-out (LOO), which is arguably much easier and not directly comparable to 10-fold CV [54] or our Patch-CV5.
“Both [38] and [39] use the evaluation protocol of “leave-one-patient-out” (LOO), which is arguably much easier and not directly comparable to 10-fold CV [54] or our Patch-CV5.”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [54]
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- Authors
- Li, Q., Cai, W., Wang, X., Zhou, Y., Feng, D. D., & Chen, M.
- Affiliations
- The University of Sydney (AU)
- Chinese University of Hong Kong (HK)
- Johns Hopkins University (US)
- Carnegie Mellon University (US)
- Venue
- 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV)
- Publisher
- IEEE
- Published
- 2014-12
- Pages
- 844-848
- OpenAlex
- W2044097773
- Cited by
- 880
- References
- 27
- Citations by year
- 2026: 28 · 2025: 90 · 2024: 103 · 2023: 128 · 2022: 133 · 2021: 140 · 2020: 91 · 2019: 71 · 2018: 40 · 2017: 34 · 2016: 18 · 2015: 4
- FWCI
- 48.66
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Control, Automation, Robotics and Vision (ICARCV) (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- interstitial lung disease, medical image classification, convolutional neural networks, automatic feature learning, texture classification
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