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.
Generative Adversarial Model improves RV segmentation accuracy in MRI.
The paper uses geometric methods to classify medical data histograms.
Privacy-preserving synthetic data from EHRs for learning and inference.
Radiomics identifies subtle cardiac changes in hypertension.
Models predict patients at risk of uncontrolled hypertension.
Bayesian optimization (BO) is a popular algorithm for solving challenging optimization tasks. It is designed for problems where the objective function is expensive to evaluate, perhaps not available in exact form, without gradient information and possibly returning noisy values. Different versions of the algorithm vary…
Proposes a novel anomaly detection method for echocardiogram videos.
Develops a model for personalized diabetes and hypertension treatment using robust regression and K-NN.
Pipeline detects pulmonary embolisms from sparse CT images.
The paper tackles optimal policy learning with asymmetric counterfactual utilities in healthcare decisions.
This work introduces an integrative approach based on Q-analysis with machine learning. The new approach, called Neural Hypernetwork, has been applied to a case study of pulmonary embolism diagnosis. The objective of the application of neural hyper-network to pulmonary embolism (PE) is to improve diagnose for reducing …
Modeling disease progression in irregularly observed patients.
Study uses multi-task Bayesian optimization to speed up SVM hyperparameter tuning for nodules diagnosis.
Motivated by the problem of computer-aided detection (CAD) of pulmonary nodules, we introduce methods to propagate and fuse uncertainty information in a multi-stage Bayesian convolutional neural network (CNN) architecture. The question we seek to answer is "can we take advantage of the model uncertainty provided by one…
Reinforcement learning improves self training for medical image segmentation.
Generating large quantities of quality labeled data in medical imaging is very time consuming and expensive. The performance of supervised algorithms for various tasks on imaging has improved drastically over the years, however the availability of data to train these algorithms have become one of the main bottlenecks f…
Computed tomography (CT) generates a stack of cross-sectional images covering a region of the body. The visual assessment of these images for the identification of potential abnormalities is a challenging and time consuming task due to the large amount of information that needs to be processed. In this article we propo…
Smartphone app diagnoses pulmonary diseases from chest X-rays.
Predicting highrisk vascular diseases is a significant issue in the medical domain. Most predicting methods predict the prognosis of patients from pathological and radiological measurements, which are expensive and require much time to be analyzed. Here we propose deep attention models that predict the onset of the hig…
Deep learning improves CVD risk prediction from health records.
Convolutional Neural Networks (CNNs) require a large amount of annotated data to learn from, which is often difficult to obtain in the medical domain. In this paper we show that the sample complexity of CNNs can be significantly improved by using 3D roto-translation group convolutions (G-Convs) instead of the more conv…
CNNs accurately measure airways and vessels on CT images, improving lung disease diagnosis.
SAPSAM trains CNNs on lung CTs with binary labels, improving CPA detection and localization.
Anatomical and biophysical modeling of left atrium (LA) and proximal pulmonary veins (PPVs) is important for clinical management of several cardiac diseases. Magnetic resonance imaging (MRI) allows qualitative assessment of LA and PPVs through visualization. However, there is a strong need for an advanced image segment…
We consider the problem in precision health of grouping people into subpopulations based on their degree of vulnerability to a risk factor. These subpopulations cannot be discovered with traditional clustering techniques because their quality is evaluated with a supervised metric: the ease of modeling a response variab…
System converts 3D lung nodule images into embeddings for retrieval.
Study compares estimators for causal mediation analysis with multiple mediators.
In this paper, we propose a new deep feature selection method based on deep architecture. Our method uses stacked auto-encoders for feature representation in higher-level abstraction. We developed and applied a novel feature learning approach to a specific precision medicine problem, which focuses on assessing and prio…
Machine learning approaches hold great potential for the automated detection of lung nodules in chest radiographs, but training the algorithms requires vary large amounts of manually annotated images, which are difficult to obtain. Weak labels indicating whether a radiograph is likely to contain pulmonary nodules are t…
It has been recently shown that sparse, nonnegative tensor factorization of multi-modal electronic health record data is a promising approach to high-throughput computational phenotyping. However, such approaches typically do not leverage available domain knowledge while extracting the phenotypes; hence, some of the su…
WEST uses EHRs and expert cases to improve rare disease phenotyping.
Proposes a tool to contrast global vs personalized models in clinical prediction.
Study uses data to analyze COPD patients' impact on hospital systems.
Traditional medicine typically applies one-size-fits-all treatment for the entire patient population whereas precision medicine develops tailored treatment schemes for different patient subgroups. The fact that some factors may be more significant for a specific patient subgroup motivates clinicians and medical researc…
Novel framework detects CKD in diabetic patients using sparse EHR representations.
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…
Lung segmentation from abnormal CXRs using data imputation.
Paper tackles cost-sensitive diagnosis and learning in healthcare, assigning feature costs based on patient discomfort.
Chronic obstructive pulmonary disease (COPD) is a lung disease where early detection benefits the survival rate. COPD can be quantified by classifying patches of computed tomography images, and combining patch labels into an overall diagnosis for the image. As labeled patches are often not available, image labels are p…
New methods improve genetic studies of complex diseases.
Lung cancer is the leading cause of cancer-related death worldwide. Early diagnosis of pulmonary nodules in Computed Tomography (CT) chest scans provides an opportunity for designing effective treatment and making financial and care plans. In this paper, we consider the problem of diagnostic classification between beni…
Atrial Fibrillation (AF) is a common electro-physiological cardiac disorder that causes changes in the anatomy of the atria. A better characterization of these changes is desirable for the definition of clinical biomarkers, furthermore, thus there is a need for its fully automatic segmentation from clinical images. In …
Improved retinal vessel segmentation with topology preservation trade-off.
PNNs improve personalized healthcare policies using mixed integer programming.
PHASE predicts surgical complications from physiological signals.
An app-based mHealth intervention uses reinforcement learning to send effective reminder notifications.
Develops a RNN model to predict obesity status improvement using irregular activity data.
Paper introduces a method to learn physics between digital twins using imperfect models.