Search Results for author:
Found 19 papers, 12 papers with code
Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
In this study, we trained a deep convolutional neural network (inception v3) on whole-slide images obtained from The Cancer Genome Atlas to accurately and automatically classify them into LUAD, LUSC or normal lung tissue.
Early-Learning Regularization Prevents Memorization of Noisy Labels
In contrast with existing approaches, which use the model output during early learning to detect the examples with clean labels, and either ignore or attempt to correct the false labels, we take a different route and instead capitalize on early learning via regularization.
Ranked #4 on
Learning with noisy labels
on CIFAR-10N-Random2
Deep EHR: Chronic Disease Prediction Using Medical Notes
Early detection of preventable diseases is important for better disease management, improved inter-ventions, and more efficient health-care resource allocation.
Variationally Regularized Graph-based Representation Learning for Electronic Health Records
A feasible approach to improving the representation learning of EHR data is to associate relevant medical concepts and utilize these connections.
On the design of convolutional neural networks for automatic detection of Alzheimer's disease
Early detection is a crucial goal in the study of Alzheimer's Disease (AD).
Multi-task Prediction of Disease Onsets from Longitudinal Lab Tests
Disparate areas of machine learning have benefited from models that can take raw data with little preprocessing as input and learn rich representations of that raw data in order to perform well on a given prediction task.
Temporal Convolutional Neural Networks for Diagnosis from Lab Tests
Early diagnosis of treatable diseases is essential for improving healthcare, and many diseases' onsets are predictable from annual lab tests and their temporal trends.
DARTS: DenseUnet-based Automatic Rapid Tool for brain Segmentation
This is also the first work to include an expert reader study to assess the quality of the segmentation obtained using a deep-learning-based model.
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time.
Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning
Our analysis reveals that models trained via our approach have higher feature reuse compared to a standard MoCo and learn informative features earlier in the network.
Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learning
The framework employs a novel self-paced sampling strategy to ensure the accuracy of pseudo labels.
Interpretable Prediction of Lung Squamous Cell Carcinoma Recurrence With Self-supervised Learning
The resulting representations and clusters from self-supervision are used as features of a survival model for recurrence prediction at the patient level.
Towards Quantification of Bias in Machine Learning for Healthcare: A Case Study of Renal Failure Prediction
We will compare and discuss the generalization and applicability of these two models, in an attempt to quantify biases of status quo clinical practice, compared to ML-driven models.
Tracing State-Level Obesity Prevalence from Sentence Embeddings of Tweets: A Feasibility Study
Twitter data has been shown broadly applicable for public health surveillance.
BERT-XML: Large Scale Automated ICD Coding Using BERT Pretraining
In this paper, we propose a machine learning model, BERT-XML, for large scale automated ICD coding from EHR notes, utilizing recently developed unsupervised pretraining that have achieved state of the art performance on a variety of NLP tasks.
Causal Effect Variational Autoencoder with Uniform Treatment
In this paper, we introduce uniform treatment variational autoencoders (UTVAE) that are trained with uniform treatment distribution using importance sampling and show that using uniform treatment over observational treatment distribution leads to better causal inference by mitigating the distribution shift that occurs from training to test time.
Deep Probability Estimation
Reliable probability estimation is of crucial importance in many real-world applications where there is inherent (aleatoric) uncertainty.
Making Self-supervised Learning Robust to Spurious Correlation via Learning-speed Aware Sampling
Self-supervised learning (SSL) has emerged as a powerful technique for learning rich representations from unlabeled data.
Automatic Detection of Alzheimer's Disease with Multi-Modal Fusion of Clinical MRI Scans
Notably, literature on the application of deep learning in the automatic detection of the disease has been proliferating.
