Search Results for author:
Found 9 papers, 2 papers with code
Smooth Attention for Deep Multiple Instance Learning: Application to CT Intracranial Hemorrhage Detection
Multiple Instance Learning (MIL) has been widely applied to medical imaging diagnosis, where bag labels are known and instance labels inside bags are unknown.
The ART of Transfer Learning: An Adaptive and Robust Pipeline
Transfer learning is an essential tool for improving the performance of primary tasks by leveraging information from auxiliary data resources.
DeepCOVID-Fuse: A Multi-modality Deep Learning Model Fusing Chest X-Radiographs and Clinical Variables to Predict COVID-19 Risk Levels
The pre-trained fusion model with only CXRs as input increases accuracy to 0. 632 and AUC to 0. 813 and with only clinical variables as input increases accuracy to 0. 539 and AUC to 0. 733.
Can Deep Learning Assist Automatic Identification of Layered Pigments From XRF Data?
The pigment identification results demonstrated that our model achieved comparable results to the analysis by elemental mapping, suggesting the generalizability and stability of our model.
Investigating the Potential of Auxiliary-Classifier GANs for Image Classification in Low Data Regimes
Generative Adversarial Networks (GANs) have shown promise in augmenting datasets and boosting convolutional neural networks' (CNN) performance on image classification tasks.
Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes
We propose a mesh-based technique to aid in the classification of Alzheimer's disease dementia (ADD) using mesh representations of the cortex and subcortical structures.
Resampling-based Confidence Intervals for Model-free Robust Inference on Optimal Treatment Regimes
We first study a smoothed robust estimator that directly targets the parameter corresponding to the Bayes decision rule for optimal treatment regimes estimation.
A Survey of Tuning Parameter Selection for High-dimensional Regression
Penalized (or regularized) regression, as represented by Lasso and its variants, has become a standard technique for analyzing high-dimensional data when the number of variables substantially exceeds the sample size.
A Comparison of 1-D and 2-D Deep Convolutional Neural Networks in ECG Classification
Then, in order to alleviate the overfitting problem in two-dimensional network, we initialize AlexNet-like network with weights trained on ImageNet, to fit the training ECG images and fine-tune the model, and to further improve the accuracy and robustness of ECG classification.
