Low-dose computed tomography (LDCT) plays a critical role in the early detection of lung cancer. Despite the life-saving benefit of early detection by LDCT, there are many limitations of this imaging modality including high rates of detection of indeterminate pulmonary nodules. Radiomics is the process of extracting and analyzing image-based, quantitative features from a region-of-interest which then can be analyzed to develop decision support tools that can improve lung cancer screening. Although prior published research has shown that delta radiomics (i.e., changes in features over time) have utility in predicting treatment response, limited work has been conducted using delta radiomics in lung cancer screening. As such, we conducted analyses to assess the performance of incorporating delta with conventional (non delta) features using machine learning to predict lung nodule malignancy. We found the best improved area under the receiver operating characteristic curve (AUC) was 0.822 when delta features were combined with conventional features versus an AUC 0.773 for conventional features only. Overall, this study demonstrated the important utility of combining delta radiomics features with conventional radiomics features to improve performance of models in the lung cancer screening setting.

Deep learning training typically starts with a random sampling initialization approach to set the weights of trainable layers. Therefore, different and/or uncontrolled weight initialization prevents learning the same model multiple times. Consequently, such models yield different results during testing. However, even with the exact same initialization for the weights, a lack of repeatability, replicability, and reproducibility may still be observed during deep learning for many reasons such as software versions, implementation variations, and hardware differences. In this paper, we study repeatability when training deep learning models for segmentation and classification tasks using U-Net and LeNet-5 architectures in two development environments Pytorch and Keras (with TensorFlow backend). We show that even with the available control of randomization in Keras and TensorFlow, there are uncontrolled randomizations. We also show repeatable results for the same deep learning architectures using the Pytorch deep learning library. Finally, we discuss variations in the implementation of the weight initialization algorithm across deep learning libraries as a source of uncontrolled error in deep learning results.

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Lack of enough labeled data is a major problem in building machine learning based models when the manual annotation (labeling) is error-prone, expensive, tedious, and time-consuming. In this paper, we introduce an iterative deep learning based method to improve segmentation and counting of cells based on unbiased stereology applied to regions of interest of extended depth of field (EDF) images. This method uses an existing machine learning algorithm called the adaptive segmentation algorithm (ASA) to generate masks (verified by a user) for EDF images to train deep learning models. Then an iterative deep learning approach is used to feed newly predicted and accepted deep learning masks/images (verified by a user) to the training set of the deep learning model. The error rate in unbiased stereology count of cells on an unseen test set reduced from about 3 % to less than 1 % after 5 iterations of the iterative deep learning based unbiased stereology process.

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