Computer Aided Diagnosis (CAD) tools are often needed for fast and accurate detection, characterization, and risk assessment of different tumors from radiology images. Any improvement in robust and accurate image-based tumor characterization can assist in determining non-invasive cancer stage, prognosis, and  personalized treatment planning as a part of precision medicine. In this study, we propose both supervised and unsupervised machine learning strategies to improve tumor characterization. Our first approach is based on supervised learning for which we demonstrate significant gains in deep learning algorithms, particularly Convolutional Neural Network (CNN), by utilizing completely 3D approach and transfer learningto address the requirements of volumetric and large amount of training data, respectively . Motivated by the radiologists' interpretations of the scans, we then show how to incorporate task dependent feature representations into a CAD system via a graph regularized sparse Multi-Task Learning (MTL)  framework. In the second approach, we explore an unsupervised scheme in order to address the limited availability of labeled training data, a common problem in medical imaging applications. Inspired by learning from label proportion (LLP) approaches, we propose a new algorithm, proportion-SVM, to characterize tumor types. In this second approach, we also seek the answer to the fundamental question about the goodness of "deep features" for unsupervised tumor classification . Finally, we study the effect of unsupervised representation learning using Generative Adversarial Networks (GAN) on classification performance . We evaluate our proposed approaches (both supervised and unsupervised) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively.
Cancer is among the leading causes of death worldwide. Risk stratification of cancer tumors in radiology images can be improved with computer-aided diagnosis (CAD) tools which can be made faster and more accurate. Tumor characterization through CADs can enable non-invasive cancer staging and prognosis, and foster personalized treatment planning as a part of precision medicine. In this study, we propose both supervised and unsupervised machine learning strategies to improve tumor characterization. Our first approach is based on supervised learning for which we demonstrate significant gains in deep learning algorithms, particularly by utilizing a 3D Convolutional Neural Network along with transfer learning . Motivated by the radiologists' interpretations of the scans, we then show how to incorporate task dependent feature representations into a CAD system via a "graph-regularized sparse Multi-Task Learning (MTL) " framework. In the second approach, we explore an unsupervised scheme to address the limited availability of labeled training data, a common problem in medical imaging applications. Inspired by learning from label proportion (LLP) approaches, we propose a new algorithm, proportion-SVM, to characterize tumor types. We also seek the answer to the fundamental question about the goodness of "deep features" for unsupervised tumor classification . We evaluate our proposed approaches (both supervised and unsupervised) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively.
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Computer Aided Diagnosis
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In this second approach, we
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. Finally, we study the effect of unsupervised representation learning using Generative Adversarial Networks (GAN) on classification performance