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Image Processing with Deep Learning at Computational Radiology Lab

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Medical-Image-Processing

This repository contains some works that I have done in Computation Radiology Lab UR Medical Center.

It mainly contains the following parts:

Keypoints of PCT-CT deep learning project

  • This project got accepted at SPIE2017 conference for oral representation. You can check out our publication here. A refined version of this paper is uploaded here.

  • It makes fully use of CaffeNet and Inception-v3 Net to classify CT samples. Please check out Deep_Transfer_Learning_PCI_CT.pdf for full detail.

  • Some result is shown below:

    The classification result we got from a fine-tuned CaffeNet.

    Keypoints of Vertebrea Segmentation project

  • Preprocessing raw spine dataset (About 20GB), you can check out the sample spine CT scan before and after preprocessing below.

  • The preprocessing techniques we have explored include: Histogram Equalization, Adaptive Histogram Equalization and Constrast Stretching

    For full detail, please refers to our report here.

  • Prepared the lmdb file so that the data can be directly fed into Convolutional Neural Network.

  • Used fully convolutional neural network to segment the image.

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