A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Deep learning for COVID-19 diagnosis using CXR images with limited data.
ECS evaluates synthetic CXR images' distributional fidelity.
Lung segmentation from abnormal CXRs using data imputation.
The MIMIC-CXR dataset is (to date) the largest released chest x-ray dataset consisting of 473,064 chest x-rays and 206,574 radiology reports collected from 63,478 patients. We present the results of training and evaluating a collection of deep convolutional neural networks on this dataset to recognize multiple common t…
Data labeling is currently a time-consuming task that often requires expert knowledge. In research settings, the availability of correctly labeled data is crucial to ensure that model predictions are accurate and useful. We propose relatively simple machine learning-based models that achieve high performance metrics in…
The chest X-ray (CXR) is by far the most commonly performed radiological examination for screening and diagnosis of many cardiac and pulmonary diseases. There is an immense world-wide shortage of physicians capable of providing rapid and accurate interpretation of this study. A radiologist-driven analysis of over two m…
Machine learning has been an emerging tool for various aspects of infectious diseases including tuberculosis surveillance and detection. However, WHO provided no recommendations on using computer-aided tuberculosis detection software because of the small number of studies, methodological limitations, and limited genera…
Study identifies COVID-19 pneumonia from chest X-rays.
The automatic detection of critical findings in chest X-rays (CXR), such as pneumothorax, is important for assisting radiologists in their clinical workflow like triaging time-sensitive cases and screening for incidental findings. While deep learning (DL) models has become a promising predictive technology with near-hu…
Deep learning ensembles improve COVID-19 detection from chest X-rays.
Study benchmarks uncertainty quantification in chest X-ray classification.
Deep learning model predicts severe COVID-19 outcomes.
Study removes bias from chest X-ray embeddings using orthogonalization.
Deep learning model improves X-ray disease detection accuracy in Thai patients.
Machine learning models for COVID-19 detection and prognosis from chest images are flawed and unreliable.
While deep learning models become more widespread, their ability to handle unseen data and generalize for any scenario is yet to be challenged. In medical imaging, there is a high heterogeneity of distributions among images based on the equipment that generates them and their parametrization. This heterogeneity trigger…
Paper detects biases in medical imaging ML models using counterfactual analysis.
A statistical toolbox for analyzing model performance in medical imaging.
Machine learning systems have received much attention recently for their ability to achieve expert-level performance on clinical tasks, particularly in medical imaging. Here, we examine the extent to which state-of-the-art deep learning classifiers trained to yield diagnostic labels from X-ray images are biased with re…
New method samples from any causal effect given conditional generative models.
While deep learning has shown promise in the domain of disease classification from medical images, models based on state-of-the-art convolutional neural network architectures often exhibit performance loss due to dataset shift. Models trained using data from one hospital system achieve high predictive performance when …
Ensemble classifier detects pneumonia patterns in chest CT images.
Zero-Shot Learning helps learn new concepts without examples, useful for COVID-19 diagnosis.