68 research outputs found
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
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
Unsupervised Deep Learning Registration of Uterine Cervix Sequence Images
During a colposcopic examination of the uterine cervix for cervical cancer prevention, one or more digital images are typically acquired after the application of diluted acetic acid. An alternative approach is to acquire a sequence of images at fixed intervals during an examination before and after applying acetic acid. This approach is asserted to be more informative as it can capture dynamic pixel intensity variations on the cervical epithelium during the aceto-whitening reaction. However, the resulting time sequence images may not be spatially aligned due to the movement of the cervix with respect to the imaging device. Disease prediction using automated visual evaluation (AVE) techniques using multiple images could be adversely impacted without correction for this misalignment. The challenge is that there is no registration ground truth to help train a supervised-learning-based image registration algorithm. We present a novel unsupervised registration approach to align a sequence of digital cervix color images. The proposed deep-learning-based registration network consists of three branches and processes the red, green, and blue (RGB, respectively) channels of each input color image separately using an unsupervised strategy. Each network branch consists of a convolutional neural network (CNN) unit and a spatial transform unit. To evaluate the registration performance on a dataset that has no ground truth, we propose an evaluation strategy that is based on comparing automatic cervix segmentation masks in the registered sequence and the original sequence. The compared segmentation masks are generated by a fine-tuned transformer-based object detection model (DeTr). The segmentation model achieved Dice/IoU scores of 0.917/0.870 and 0.938/0.885, which are comparable to the performance of our previous model in two datasets. By comparing our segmentation on both original and registered time sequence images, we observed an average improvement in Dice scores of 12.62% following registration. Further, our approach achieved higher Dice and IoU scores and maintained full image integrity compared to a non-deep learning registration method on the same dataset
A Deep Modality-Specific Ensemble for Improving Pneumonia Detection in Chest X-rays
Pneumonia is an acute respiratory infectious disease caused by bacteria, fungi, or viruses. Fluid-filled lungs due to the disease result in painful breathing difficulties and reduced oxygen intake. Effective diagnosis is critical for appropriate and timely treatment and improving survival. Chest X-rays (CXRs) are routinely used to screen for the infection. Computer-aided detection methods using conventional deep learning (DL) models for identifying pneumonia-consistent manifestations in CXRs have demonstrated superiority over traditional machine learning approaches. However, their performance is still inadequate to aid in clinical decision-making. This study improves upon the state of the art as follows. Specifically, we train a DL classifier on large collections of CXR images to develop a CXR modality-specific model. Next, we use this model as the classifier backbone in the RetinaNet object detection network. We also initialize this backbone using random weights and ImageNet-pretrained weights. Finally, we construct an ensemble of the best-performing models resulting in improved detection of pneumonia-consistent findings. Experimental results demonstrate that an ensemble of the top-3 performing RetinaNet models outperformed individual models in terms of the mean average precision (mAP) metric (0.3272, 95% CI: (0.3006,0.3538)) toward this task, which is markedly higher than the state of the art (mAP: 0.2547). This performance improvement is attributed to the key modifications in initializing the weights of classifier backbones and constructing model ensembles to reduce prediction variance compared to individual constituent models
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