Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches (1801.03230v3)

Abstract: Risk stratification (characterization) of tumors from radiology images can be more accurate and faster with computer-aided diagnosis (CAD) tools. Tumor characterization through such tools can also enable non-invasive cancer staging, 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 with deep learning algorithms, particularly by utilizing a 3D Convolutional Neural Network and 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 learning algorithm to address the limited availability of labeled training data, a common problem in medical imaging applications. Inspired by learning from label proportion (LLP) approaches in computer vision, we propose to use proportion-SVM for characterizing tumors. We also seek the answer to the fundamental question about the goodness of "deep features" for unsupervised tumor classification. We evaluate our proposed supervised and unsupervised learning algorithms on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans, respectively, and obtain the state-of-the-art sensitivity and specificity results in both problems.

Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches

This paper presents a dual-faceted exploration into the characterization of lung and pancreatic tumors utilizing the transformative capacities of machine learning techniques. The profound challenges in medical imaging, such as the necessity for accurate and rapid tumor characterization, are approached by leveraging both supervised and unsupervised learning models. This work provides a systematic blueprint, employing novel algorithms and architecture to address these challenges.

The research delineates the use of a 3D Convolutional Neural Network (CNN) alongside transfer learning for supervised tumor classification. The authors advocate the potent use of a pretrained 3D CNN—originally trained on a massive natural video dataset—and fine-tune it with CT scans of lung nodules. The integration of domain knowledge through task-specific lung nodule attributes, expressed within a graph-regularized sparse Multi-Task Learning (MTL) framework, stands out as a strategic advantage. This MTL framework facilitates the incorporation of complementary features to enhance the separation between benign and malignant lesions, achieving superior accuracy and reducing the malignancy score prediction error.

In the unsupervised learning paradigm, the paper introduces an intriguing approach by employing clustering to assign initial tumor labels, followed by refinement through Proportion-Support Vector Machine (Proportion-SVM). This method is particularly adept at overcoming the challenge of limited labeled data, which is a pervasive issue in medical image diagnostics. The efficacy of such unsupervised learning strategies is underscored by state-of-the-art results in both lung and pancreatic tumor characterization, identified through comprehensive evaluation metrics, including sensitivity and specificity.

Numerical results exhibit the 3D CNN-based supervised learning approach producing significant improvements in prediction accuracy, outperforming traditional methods and confirming its utility in clinical settings. Similarly, the unsupervised method demonstrated competent classification performance, furnishing a practical alternative when labeled datasets are scarce.

The theoretical implications of this paper resonate along several axes. Firstly, it enriches the repertoire of machine learning with graph-based MTL models tailored for nuanced diagnostic tasks. Secondly, it lays groundwork for prospective advancements in unsupervised learning, pointedly relevant in low-data environments, aligning with semi-supervised domains like Multiple Instance Learning (MIL) or active learning strategies.

Practically, the paper intimates a favorable trajectory for integrating machine learning-driven solutions in clinical workflows, potentially reducing the misdiagnosis rate and optimizing treatment planning. The exploration into pancreatic tumors, specifically using MRI data for IPMN characterization, marks a significant stride towards integrating machine learning into broader cancer diagnosis frameworks.

Future research could build on the nascent insights offered here, delving deeper into unsupervised feature learning models such as GANs to bolster classification without heavy reliance on annotated data. Further, expanding multi-modality deep learning architectures could harness diverse imaging data, providing a richer scope for early and accurate tumor detection.

This paper remains a valuable contribution to the domain, setting a precedent for computational methods in tumor characterization—essential in the ongoing efforts to augment precision medicine and improve outcomes for cancer patients.