Generalizability of Deep Adult Lung Segmentation Models to the Pediatric Population: A Retrospective Study

Lung segmentation in chest X-rays (CXRs) is an important prerequisite for improving the specificity of diagnoses of cardiopulmonary diseases in a clinical decision support system. Current deep learning (DL) models for lung segmentation are trained and evaluated on CXR datasets in which the radiographic projections are captured predominantly from the adult population. However, the shape of the lungs is reported to be significantly different for pediatrics across the developmental stages from infancy to adulthood. This might result in age-related data domain shifts that would adversely impact lung segmentation performance when the models trained on the adult population are deployed for pediatric lung segmentation. In this work, our goal is to analyze the generalizability of deep adult lung segmentation models to the pediatric population and improve performance through a systematic combinatorial approach consisting of CXR modality-specific weight initializations, stacked generalization, and an ensemble of the stacked generalization models. Novel evaluation metrics consisting of Mean Lung Contour Distance and Average Hash Score are proposed in addition to the Multi-scale Structural Similarity Index Measure, Intersection of Union, and Dice metrics to evaluate segmentation performance. We observed a significant improvement (p < 0.05) in cross-domain generalization through our combinatorial approach. This study could serve as a paradigm to analyze the cross-domain generalizability of deep segmentation models for other medical imaging modalities and applications.Comment: 11 pages, 7 figures, and 8 table

Synthetic Sample Selection via Reinforcement Learning

Synthesizing realistic medical images provides a feasible solution to the shortage of training data in deep learning based medical image recognition systems. However, the quality control of synthetic images for data augmentation purposes is under-investigated, and some of the generated images are not realistic and may contain misleading features that distort data distribution when mixed with real images. Thus, the effectiveness of those synthetic images in medical image recognition systems cannot be guaranteed when they are being added randomly without quality assurance. In this work, we propose a reinforcement learning (RL) based synthetic sample selection method that learns to choose synthetic images containing reliable and informative features. A transformer based controller is trained via proximal policy optimization (PPO) using the validation classification accuracy as the reward. The selected images are mixed with the original training data for improved training of image recognition systems. To validate our method, we take the pathology image recognition as an example and conduct extensive experiments on two histopathology image datasets. In experiments on a cervical dataset and a lymph node dataset, the image classification performance is improved by 8.1% and 2.3%, respectively, when utilizing high-quality synthetic images selected by our RL framework. Our proposed synthetic sample selection method is general and has great potential to boost the performance of various medical image recognition systems given limited annotation.Comment: MICCAI202

Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric Chest X-ray images

Model initialization techniques are vital for improving the performance and reliability of deep learning models in medical computer vision applications. While much literature exists on non-medical images, the impacts on medical images, particularly chest X-rays (CXRs) are less understood. Addressing this gap, our study explores three deep model initialization techniques: Cold-start, Warm-start, and Shrink and Perturb start, focusing on adult and pediatric populations. We specifically focus on scenarios with periodically arriving data for training, thereby embracing the real-world scenarios of ongoing data influx and the need for model updates. We evaluate these models for generalizability against external adult and pediatric CXR datasets. We also propose novel ensemble methods: F-score-weighted Sequential Least-Squares Quadratic Programming (F-SLSQP) and Attention-Guided Ensembles with Learnable Fuzzy Softmax to aggregate weight parameters from multiple models to capitalize on their collective knowledge and complementary representations. We perform statistical significance tests with 95% confidence intervals and p-values to analyze model performance. Our evaluations indicate models initialized with ImageNet-pre-trained weights demonstrate superior generalizability over randomly initialized counterparts, contradicting some findings for non-medical images. Notably, ImageNet-pretrained models exhibit consistent performance during internal and external testing across different training scenarios. Weight-level ensembles of these models show significantly higher recall (p<0.05) during testing compared to individual models. Thus, our study accentuates the benefits of ImageNet-pretrained weight initialization, especially when used with weight-level ensembles, for creating robust and generalizable deep learning solutions.Comment: 40 pages, 8 tables, 7 figures, 3 supplementary figures and 4 supplementary table

Spatial-temporal distribution and potential risk of pesticides in ambient air in the North China Plain

The intensive use of pesticides in the North China Plain (NCP) has resulted in widespread contamination of pesticides in the local atmosphere, posing risks to air quality and human health. However, the occurrence and distribution of atmospheric pesticides in the NCP as well as their risk assessment have not been well investigated. In this study, 300 monthly samples were collected using passive air samplers with polyurethane foam at ten rural sites with different crop systems in Quzhou county, the NCP, from June 2021 to May 2022. The pesticides were quantified using mass-spectrometric techniques. Our results revealed that chlorpyrifos, carbendazim, and atrazine were the most frequently found pesticides in the air samples, with detection frequencies of ≥ 87 % across the samples. The average concentrations of atmospheric pesticides during spring (7.47 pg m-3) and summer (16.05 pg m-3) were significantly higher than those during autumn (2.04 pg m-3) and winter (1.71 pg m-3), attributable to the intensified application of pesticides during the warmer seasons. Additionally, cash crop sites exhibited higher concentrations (10.26 pg m-3) of atmospheric pesticides compared to grain crop (5.59 pg m-3) and greenhouse sites (3.81 pg m-3), primarily due to more frequent pesticides spraying events in cash crop fields. These findings indicate a distinct spatial-temporal distribution pattern of atmospheric pesticides influenced by both seasons and crop systems. Furthermore, the model-based inhalation risk assessment indicates that inhalation exposure to atmospheric pesticides is unlikely to pose a significant public concern

Computerized detection of abnormalities in endoscopic oesophageal images

The research work comprises four segments: extracting lumen boundary, detecting lumen related abnormalities, forming a suitable colour segmentation framework, and classifying abnormalities using colour characteristics, applied to oesophageal images. Considering that an accurate determination of lumen boundary is important in the navigation of the endoscope in oesophagoscopy, a new method is proposed for automatic detection and extraction of lumen boundary of oesophagus. The proposed technique involves pre-processing, histogram analysis, region growing, post-processing,Master of Engineerin