Exploring texture Transfer Learning for Colonic Polyp Classification via Convolutional Neural Networks

This work addresses Transfer Learning via Convolutional Neural Networks (CNN's) for the automated classification of colonic polyps in eight HD-endoscopic image databases acquired using different modalities. For this purpose, we explore if the architecture, the training approach, the number of classes, the number of images as well as the nature of the images in the training phase can influence the results. The experiments show that when the number of classes and the nature of the images are similar to the target database, the results are improved. Also, the better results obtained by the transfer learning compared to the most used features in the literature suggest that features learned by CNN's can be highly relevant for automated classification of colonic polyps.

[1]  Andreas Uhl,et al.  Directional wavelet based features for colonic polyp classification , 2016, Medical Image Anal..

[2]  Andrea Vedaldi,et al.  MatConvNet: Convolutional Neural Networks for MATLAB , 2014, ACM Multimedia.

[3]  Andreas Uhl,et al.  Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification , 2016, Comput. Math. Methods Medicine.

[4]  Eduardo Ferreira Ribeiro,et al.  Image Characterization via Multilayer Neural Networks , 2008, 2008 20th IEEE International Conference on Tools with Artificial Intelligence.

[5]  Andreas Uhl,et al.  A Novel Shape Feature Descriptor for the Classification of Polyps in HD Colonoscopy , 2013, MCV.

[6]  Hayit Greenspan,et al.  Chest pathology detection using deep learning with non-medical training , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[7]  Shinji Tanaka,et al.  Local fractal dimension based approaches for colonic polyp classification , 2015, Medical Image Anal..

[8]  S. Kudo,et al.  Colorectal tumours and pit pattern. , 1994, Journal of clinical pathology.

[9]  Xuelong Li,et al.  Texture Classification and Retrieval Using Shearlets and Linear Regression , 2015, IEEE Transactions on Cybernetics.

[10]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Pietro Perona,et al.  Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[12]  Andrew Zisserman,et al.  Return of the Devil in the Details: Delving Deep into Convolutional Nets , 2014, BMVC.

[13]  Andreas Uhl,et al.  Evaluation of cross-validation protocols for the classification of endoscopic images of colonic polyps , 2012, 2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS).

[14]  Gertjan J. Burghouts,et al.  Material-specific adaptation of color invariant features , 2009, Pattern Recognit. Lett..

[15]  Shree K. Nayar,et al.  Reflectance and texture of real-world surfaces , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Yasushi Sano,et al.  Magnifying colonoscopy as a non-biopsy technique for differential diagnosis of non-neoplastic and neoplastic lesions. , 2006, World journal of gastroenterology.

[17]  Andreas Uhl,et al.  Color treatment in endoscopic image classification using multi-scale local color vector patterns , 2012, Medical Image Anal..

[18]  Bram van Ginneken,et al.  Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[19]  Iasonas Kokkinos,et al.  Describing Textures in the Wild , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[21]  Miguel Ángel Guevara-López,et al.  Convolutional neural networks for mammography mass lesion classification , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[22]  A. Uhl,et al.  Fully automated decision support systems for celiac disease diagnosis , 2016 .

[23]  Andreas Uhl,et al.  Transfer Learning for Colonic Polyp Classification Using Off-the-Shelf CNN Features , 2016, CARE@MICCAI.