Nodule detection and generation on chest X-rays: NODE21 Challenge.

Pulmonary nodules may be an early manifestation of lung cancer, the leading cause of cancer-related deaths among both men and women. Numerous studies have established that deep learning methods can yield high-performance levels in the detection of lung nodules in chest X-rays. However, the lack of gold-standard public datasets slows down the progression of the research and prevents benchmarking of methods for this task. To address this, we organized a public research challenge, NODE21, aimed at the detection and generation of lung nodules in chest X-rays. While the detection track assesses state-of-the-art nodule detection systems, the generation track determines the utility of nodule generation algorithms to augment training data and hence improve the performance of the detection systems. This paper summarizes the results of the NODE21 challenge and performs extensive additional experiments to examine the impact of the synthetically generated nodule training images on the detection algorithm performance.

FRODO: An In-Depth Analysis of a System to Reject Outlier Samples From a Trained Neural Network.

An important limitation of state-of-the-art deep learning networks is that they do not recognize when their input is dissimilar to the data on which they were trained and proceed to produce outputs that will be unreliable or nonsensical. In this work, we describe FRODO (Free Rejection of Out-of-Distribution), a publicly available method that can be easily employed for any trained network to detect input data from a different distribution than is expected. FRODO uses the statistical distribution of intermediate layer outputs to define the expected in-distribution (ID) input image properties. New samples are judged based on the Mahalanobis distance (MD) of their layer outputs from the defined distribution. The method can be applied to any network, and we demonstrate the performance of FRODO in correctly rejecting OOD samples on three distinct architectures for classification, localization, and segmentation tasks in chest X-rays. A dataset of 21,576 X-ray images with 3,655 in-distribution samples is defined for testing. The remaining images are divided into four OOD categories of varying levels of difficulty, and performance at rejecting each type is evaluated using receiver operating characteristic (ROC) analysis. FRODO achieves areas under the ROC (AUC) of between 0.815 and 0.999 in distinguishing OOD samples of different types. This is shown to be comparable with the best-performing state-of-the-art method tested, with the substantial advantage that FRODO integrates seamlessly with any network and requires no extra model to be constructed and trained.

Knowledge distillation with ensembles of convolutional neural networks for medical image segmentation.

Purpose: Ensembles of convolutional neural networks (CNNs) often outperform a single CNN in medical image segmentation tasks, but inference is computationally more expensive and makes ensembles unattractive for some applications. We compared the performance of differently constructed ensembles with the performance of CNNs derived from these ensembles using knowledge distillation, a technique for reducing the footprint of large models such as ensembles. Approach: We investigated two different types of ensembles, namely, diverse ensembles of networks with three different architectures and two different loss-functions, and uniform ensembles of networks with the same architecture but initialized with different random seeds. For each ensemble, additionally, a single student network was trained to mimic the class probabilities predicted by the teacher model, the ensemble. We evaluated the performance of each network, the ensembles, and the corresponding distilled networks across three different publicly available datasets. These included chest computed tomography scans with four annotated organs of interest, brain magnetic resonance imaging (MRI) with six annotated brain structures, and cardiac cine-MRI with three annotated heart structures. Results: Both uniform and diverse ensembles obtained better results than any of the individual networks in the ensemble. Furthermore, applying knowledge distillation resulted in a single network that was smaller and faster without compromising performance compared with the ensemble it learned from. The distilled networks significantly outperformed the same network trained with reference segmentation instead of knowledge distillation. Conclusion: Knowledge distillation can compress segmentation ensembles of uniform or diverse composition into a single CNN while maintaining the performance of the ensemble.

Automated estimation of total lung volume using chest radiographs and deep learning.

BACKGROUND: Total lung volume is an important quantitative biomarker and is used for the assessment of restrictive lung diseases. PURPOSE: In this study, we investigate the performance of several deep-learning approaches for automated measurement of total lung volume from chest radiographs. METHODS: About 7621 posteroanterior and lateral view chest radiographs (CXR) were collected from patients with chest CT available. Similarly, 928 CXR studies were chosen from patients with pulmonary function test (PFT) results. The reference total lung volume was calculated from lung segmentation on CT or PFT data, respectively. This dataset was used to train deep-learning architectures to predict total lung volume from chest radiographs. The experiments were constructed in a stepwise fashion with increasing complexity to demonstrate the effect of training with CT-derived labels only and the sources of error. The optimal models were tested on 291 CXR studies with reference lung volume obtained from PFT. Mean absolute error (MAE), mean absolute percentage error (MAPE), and Pearson correlation coefficient (Pearson's r) were computed. RESULTS: The optimal deep-learning regression model showed an MAE of 408 ml and an MAPE of 8.1% using both frontal and lateral chest radiographs as input. The predictions were highly correlated with the reference standard (Pearson's r = 0.92). CT-derived labels were useful for pretraining but the optimal performance was obtained by fine-tuning the network with PFT-derived labels. CONCLUSION: We demonstrate, for the first time, that state-of-the-art deep-learning solutions can accurately measure total lung volume from plain chest radiographs. The proposed model is made publicly available and can be used to obtain total lung volume from routinely acquired chest radiographs at no additional cost. This deep-learning system can be a useful tool to identify trends over time in patients referred regularly for chest X-ray.

Deep learning for chest X-ray analysis: A survey.

Recent advances in deep learning have led to a promising performance in many medical image analysis tasks. As the most commonly performed radiological exam, chest radiographs are a particularly important modality for which a variety of applications have been researched. The release of multiple, large, publicly available chest X-ray datasets in recent years has encouraged research interest and boosted the number of publications. In this paper, we review all studies using deep learning on chest radiographs published before March 2021, categorizing works by task: image-level prediction (classification and regression), segmentation, localization, image generation and domain adaptation. Detailed descriptions of all publicly available datasets are included and commercial systems in the field are described. A comprehensive discussion of the current state of the art is provided, including caveats on the use of public datasets, the requirements of clinically useful systems and gaps in the current literature.