Deep Multi-instance Volumetric Image Classification with Extreme Value Distributions

Predicting the presence of a disease in volumetric images is an essential task in medical imaging. The use of state-of-the-art techniques like deep convolutional neural networks (CNN) for such tasks is challenging due to limited supervised training data and high memory usage. This paper presents a weakly supervised solution that can be used in learning deep CNN features for volumetric image classification. In the proposed method, we use extreme value theory to model the feature distribution of the images without a pathology and use it to identify positive instances in an image that contains pathology. The experimental results show that the proposed method can learn classifiers that have similar performance to a fully supervised method and have significantly better performance in comparison with methods that use fixed number of instances from a positive image.

[1]  Joel H. Saltz,et al.  Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  S. Coles,et al.  An Introduction to Statistical Modeling of Extreme Values , 2001 .

[3]  Mohsen Guizani,et al.  Deep Features Learning for Medical Image Analysis with Convolutional Autoencoder Neural Network , 2017 .

[4]  L. Haan,et al.  On the block maxima method in extreme value theory: PWM estimators , 2013, 1310.3222.

[5]  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.

[6]  Shu Liao,et al.  Multi-Instance Deep Learning: Discover Discriminative Local Anatomies for Bodypart Recognition , 2016, IEEE Transactions on Medical Imaging.

[7]  Michael F. Marmor,et al.  Mechanisms of fluid accumulation in retinal edema , 2004, Documenta Ophthalmologica.

[8]  Terrance E. Boult,et al.  Towards Open Set Deep Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Guy Cazuguel,et al.  Multiple-Instance Learning for Medical Image and Video Analysis , 2017, IEEE Reviews in Biomedical Engineering.

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

[11]  S. Berman.  Limiting Distribution of the Maximum Term in Sequences of Dependent Random Variables , 1962 .

[12]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[13]  Thomas Hofmann,et al.  Support Vector Machines for Multiple-Instance Learning , 2002, NIPS.

[14]  Anderson Rocha,et al.  Toward Open Set Recognition , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Lauge Sørensen,et al.  Texture-Based Analysis of COPD: A Data-Driven Approach , 2012, IEEE Transactions on Medical Imaging.

[16]  Xiaohui Xie,et al.  Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification , 2016, bioRxiv.

[17]  Hao Chen,et al.  Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks , 2016, IEEE Transactions on Medical Imaging.

[18]  Anderson Rocha,et al.  Meta-Recognition: The Theory and Practice of Recognition Score Analysis , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Bram van Ginneken,et al.  Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks , 2016, IEEE Transactions on Medical Imaging.

[20]  Lauge Sørensen,et al.  Classification of COPD with Multiple Instance Learning , 2014, 2014 22nd International Conference on Pattern Recognition.