Hanyang Med Rev.  2017 Nov;37(2):71-76. 10.7599/hmr.2017.37.2.71.

Deep Learning for Cancer Screening in Medical Imaging

• Jeong J

• KMID: 2424831
• DOI: http://doi.org/10.7599/hmr.2017.37.2.71

Abstract

In recent years, deep learning has been used in many researches in cancer screening based on medical imaging. Among cancer screening using optical imaging, melanoma detection is the biggest concern. Stanford University researchers used CNNs (convolutional neural networks) to classify skin lesions comparing with 21 dermatologists for 2 tasks. CNN performed better than all the dermatologists' tasks. Finding pulmonary nodules on chest X-ray has the longest history in cancer screening using medical imaging and neural network technology began to be applied before the deep learning technology matured as it is now. But, the applications were mainly focused on screening in CT images. There is relatively few research on pulmonary nodule detection using deep learning in chest X-rays. For breast cancer screening in mammography, adoption of neural network technologies has already begun early. Many studies have shown that tumor detection using CNNs is useful in breast cancer screening. Most of the results are from mammography, but studies using tomosynthesis, ultrasound, and MRI have also been published. Although imaging modality and target cancer are different, we can see that there are similar kinds of future challenges. First, it is not easy to acquire a large amount of medical image data required for deep learning. Second, it is difficult to learn if there are many medical image data but they are not properly labeled. Finally, there is a need for technologies that can use different imaging modalities at the same time, link with electronic health records, and use genetic information for more comprehensive screening.

Keyword

Deep Learning; Convolutional Neural Networks; CNNs; Computer Aided Diagnosis; CAD; Skin Cancer; Mammography; Chest X-ray

MeSH Terms

Breast Neoplasms
Diagnostic Imaging*
Early Detection of Cancer*
Electronic Health Records
Learning*
Magnetic Resonance Imaging
Mammography
Mass Screening
Melanoma
Optical Imaging
Skin
Skin Neoplasms
Thorax
Ultrasonography

Figure

• 

  Fig. 1 Three types of learning scenarios. Fully-supervised learning if we can obtain medical image data with full annotations, weakly-supervised learning if we have no annotations. Semi-weakly-supervised lea rning can be applied in between. We need to determine α (percentage of annotated medical images) reasonably depending on the data acquisition and performance of the training and test.

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