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Found 14 papers, 0 papers with code
Attend and Diagnose: Clinical Time Series Analysis using Attention Models
With widespread adoption of electronic health records, there is an increased emphasis for predictive models that can effectively deal with clinical time-series data.
A Generative Modeling Approach to Limited Channel ECG Classification
Processing temporal sequences is central to a variety of applications in health care, and in particular multi-channel Electrocardiogram (ECG) is a highly prevalent diagnostic modality that relies on robust sequence modeling.
Understanding Behavior of Clinical Models under Domain Shifts
The hypothesis that computational models can be reliable enough to be adopted in prognosis and patient care is revolutionizing healthcare.
Generalization Studies of Neural Network Models for Cardiac Disease Detection Using Limited Channel ECG
Though deep neural networks have achieved unprecedented success in predictive modeling, they rely solely on discriminative models that can generalize poorly to unseen classes.
Leveraging Medical Visual Question Answering with Supporting Facts
In this working notes paper, we describe IBM Research AI (Almaden) team's participation in the ImageCLEF 2019 VQA-Med competition.
Pi-PE: A Pipeline for Pulmonary Embolism Detection using Sparsely Annotated 3D CT Images
Pulmonary embolisms (PE) are known to be one of the leading causes for cardiac-related mortality.
Learn-By-Calibrating: Using Calibration as a Training Objective
Calibration error is commonly adopted for evaluating the quality of uncertainty estimators in deep neural networks.
Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models
The wide-spread adoption of representation learning technologies in clinical decision making strongly emphasizes the need for characterizing model reliability and enabling rigorous introspection of model behavior.
Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification
Automated diagnostic assistants in healthcare necessitate accurate AI models that can be trained with limited labeled data, can cope with severe class imbalances and can support simultaneous prediction of multiple disease conditions.
Automatic Diagnosis of Pulmonary Embolism Using an Attention-guided Framework: A Large-scale Study
In addition to the classification loss, an attention loss was added during training to help the network focus attention on PE.
Loss Estimators Improve Model Generalization
With increased interest in adopting AI methods for clinical diagnosis, a vital step towards safe deployment of such tools is to ensure that the models not only produce accurate predictions but also do not generalize to data regimes where the training data provide no meaningful evidence.
Designing Counterfactual Generators using Deep Model Inversion
Explanation techniques that synthesize small, interpretable changes to a given image while producing desired changes in the model prediction have become popular for introspecting black-box models.
Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors
We focus on the problem of producing well-calibrated out-of-distribution (OOD) detectors, in order to enable safe deployment of medical image classifiers.
Self-training of Machine Learning Models for Liver Histopathology: Generalization under Clinical Shifts
Histopathology images are gigapixel-sized and include features and information at different resolutions.
