Towards Novel Methods for Effective Transfer Learning and Unsupervised Deep Learning for Medical Image Analysis

Mijung Kim, Jasper Zuallaert, Wesley De Neve


Increasing attention to the deep learning applications comes to the medical image analysis as well. Recently, Google published the paper on detecting the diabetic retinopathy using their deep learning approach. During our doctoral research, we will mainly focus on dealing with medical image dataset from the deep learning's perspective. In particular, due to its sensitive nature ans sparsity, medical image dataset does not show high performance as other images do such as ImageNet. By making use of transfer learning and unsupervised learning techniques we will investigate in increasing the effectiveness our models in medical image dataset. As for the first step, we challenge to the breast cancer mammography dataset using Inception V4 to diagnose the lesions. After applying data augmentation methods we will move on to unsupervised learning approach to overcome small size and unlabeled dataset. Ultimately, we will develop a unsupervised deep learning network with transferable discriminator maintaining high effectiveness.


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Paper Citation

in Harvard Style

Kim M., Zuallaert J. and De Neve W. (2017). Towards Novel Methods for Effective Transfer Learning and Unsupervised Deep Learning for Medical Image Analysis . In Doctoral Consortium - DCBIOSTEC, (BIOSTEC 2017) ISBN , pages 32-39

in Bibtex Style

author={Mijung Kim and Jasper Zuallaert and Wesley De Neve},
title={Towards Novel Methods for Effective Transfer Learning and Unsupervised Deep Learning for Medical Image Analysis},
booktitle={Doctoral Consortium - DCBIOSTEC, (BIOSTEC 2017)},

in EndNote Style

JO - Doctoral Consortium - DCBIOSTEC, (BIOSTEC 2017)
TI - Towards Novel Methods for Effective Transfer Learning and Unsupervised Deep Learning for Medical Image Analysis
SN -
AU - Kim M.
AU - Zuallaert J.
AU - De Neve W.
PY - 2017
SP - 32
EP - 39
DO -