Deep Learning in Medical Imaging
Deep learning for structures detection. Localization and interpolation of anatomical structures in medical images is a key step in radiological workflow.
Medical Image Analysis is currently experiencing a paradigm shift due to Deep Learning. This technology has recently attracted so much interest of the Medical Imaging community that it led to a specialized conference in ‘Medical Imaging with Deep Learning’ in the year 2018. This article surveys the recent developments in this direction, and provides a critical review of the related major aspects. We organize the reviewed literature according to the underlying Pattern Recognition tasks, and further sub-categorize it following a taxonomy based on human anatomy. This article does not assume prior knowledge of Deep Learning and makes a significant contribution in explaining the core Deep Learning concepts to the non-experts in the Medical community. Unique to this study is the Computer Vision/Machine Learning perspective taken on the advances of Deep Learning in Medical Imaging. This enables us to single out ‘lack of appropriately annotated large-scale datasets’ as the core challenge (among other challenges) in this research direction. We draw on the insights from the sister research fields of Computer Vision, Pattern Recognition and Machine Learning etc.; where the techniques of dealing with such challenges have already matured, to provide promising directions for the Medical Imaging community to fully harness Deep Learning in the future.
The development of artificial intelligence (AI) enables new approaches to image analysis. AI models have been shown to recognize histological UIP pattern by using genomic data from lung biopsies. Radiological findings can also be quantitated using automated image analysis and have been associated with pulmonary function, survival, and response to antifibrotic medication. In a manner comparable to radiologists, an AI model can classify fibrotic lung diseases according to high-resolution computed tomography images. AI models have been used in the histology of experimental mouse models of pulmonary fibrosis. To our knowledge, histological features of IPF samples have not been previously studied using automated image analysis. Before developing diagnostic AI models for the UIP pattern, the ability of AI to identify specific histological features should be tested.
We aim to test the previous association between FF and prognosis of patients with IPF using the automated image analysis. Our approach was to pilot an AI model with a small data set and test its generalizability in slides that were not included in the training data set. Using lung tissue samples of thoroughly characterized patients from the Finnish IPF registry patients, we develop the AI model with a deep convolutional neural network (CNN).
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