Deep Medical Image Computing in Preventive and Precision Medicine

Deep learning has a game-changing potential to improve the state of preventative and precision medicine within medical US Research Labs image computing. Here, we will first overview preventative and precision medicine and field of deep learning. Afterward, we will share our perspective on recent research and development NVIDIA activities in both areas and point out some existing achievements, positive indications, limitations, and near future opportunities and impediments. To flesh out our viewpoints, we draw from examples of our most recent work, which largely stem from radiologic images, but we encourage readers to consult some other recent reviews, which include many references that space did not allow us to include. We also assume the reader is broadly familiar with machine learning technologies.

[1]  Ronald M. Summers,et al.  Spatial aggregation of holistically‐nested convolutional neural networks for automated pancreas localization and segmentation☆ , 2017, Medical Image Anal..

[2]  Ronald M. Summers,et al.  Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-Scale Lesion Database , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[3]  Le Lu,et al.  Improving Deep Pancreas Segmentation in CT and MRI Images via Recurrent Neural Contextual Learning and Direct Loss Function , 2017, ArXiv.

[4]  Ronald M. Summers,et al.  Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning , 2016, IEEE Transactions on Medical Imaging.

[5]  Youbao Tang,et al.  Accurate Weakly Supervised Deep Lesion Segmentation on CT Scans: Self-Paced 3D Mask Generation from RECIST , 2018, ArXiv.

[6]  Ronald M. Summers,et al.  Unsupervised Joint Mining of Deep Features and Image Labels for Large-Scale Radiology Image Categorization and Scene Recognition , 2017, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).

[7]  Ludmila V. Danilova,et al.  Detection and localization of surgically resectable cancers with a multi-analyte blood test , 2018, Science.

[8]  Ronald M. Summers,et al.  Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation , 2015, IEEE Transactions on Medical Imaging.

[9]  Ronald M. Summers,et al.  Progressive and Multi-path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images , 2017, MICCAI.

[10]  ShinHoo-Chang,et al.  Interleaved text/image deep mining on a large-scale radiology database for automated image interpretation , 2016 .

[11]  Ronald M. Summers,et al.  Convolutional Invasion and Expansion Networks for Tumor Growth Prediction , 2018, IEEE Transactions on Medical Imaging.

[12]  Ronald M. Summers,et al.  ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases , 2019, Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics.