Rapid Training Data Creation by Synthesizing Medical Images for Classification and Localization

While the use of artificial intelligence (AI) for medical image analysis is gaining wide acceptance, the expertise, time and cost required to generate annotated data in the medical field are significantly high, due to limited availability of both data and expert annotation. Strongly supervised object localization models require data that is exhaustively annotated, meaning all objects of interest in an image are identified. This is difficult to achieve and verify for medical images. We present a method for the transformation of real data to train any Deep Neural Network to solve the above problems. We show the efficacy of this approach on both a weakly supervised localization model and a strongly supervised localization model. For the weakly supervised model, we show that the localization accuracy increases significantly using the generated data. For the strongly supervised model, this approach overcomes the need for exhaustive annotation on real images. In the latter model, we show that the accuracy, when trained with generated images, closely parallels the accuracy when trained with exhaustively annotated real images. The results are demonstrated on images of human urine samples obtained using microscopy.

[1]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Peter I. Corke,et al.  Sim-to-real Transfer of Visuo-motor Policies for Reaching in Clutter: Domain Randomization and Adaptation with Modular Networks , 2017, ArXiv.

[3]  Antonio M. López,et al.  The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Stephen James,et al.  3D Simulation for Robot Arm Control with Deep Q-Learning , 2016, ArXiv.

[5]  Bolei Zhou,et al.  Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[7]  Michael J. Black,et al.  A Naturalistic Open Source Movie for Optical Flow Evaluation , 2012, ECCV.

[8]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[9]  Thomas Brox,et al.  FlowNet: Learning Optical Flow with Convolutional Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[10]  Kate Saenko,et al.  Learning Deep Object Detectors from 3D Models , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

[11]  Andrew J. Davison,et al.  Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task , 2017, CoRL.

[12]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Roberto Cipolla,et al.  SceneNet: Understanding Real World Indoor Scenes With Synthetic Data , 2015, ArXiv.

[14]  Ankush Gupta,et al.  Synthetic Data for Text Localisation in Natural Images , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Martial Hebert,et al.  Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[16]  Qiao Wang,et al.  VirtualWorlds as Proxy for Multi-object Tracking Analysis , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Jiaying Liu,et al.  Revisiting Batch Normalization For Practical Domain Adaptation , 2016, ICLR.

[18]  Bernard Ghanem,et al.  UE4Sim: A Photo-Realistic Simulator for Computer Vision Applications , 2017, ArXiv.

[19]  Xiaolin Hu,et al.  UnrealStereo: A Synthetic Dataset for Analyzing Stereo Vision , 2016, ArXiv.