Search Results for author:
Found 23 papers, 6 papers with code
Spatiotemporal Modeling of Multivariate Signals With Graph Neural Networks and Structured State Space Models
Multivariate signals are prevalent in various domains, such as healthcare, transportation systems, and space sciences.
TRUST-LAPSE: An Explainable & Actionable Mistrust Scoring Framework for Model Monitoring
We show that our sequential mistrust scores achieve high drift detection rates: over 90% of the streams show < 20% error for all domains.
Supervised Machine Learning Algorithm for Detecting Consistency between Reported Findings and the Conclusions of Mammography Reports
Our term normalization algorithm correctly identified 97% of the BI-RADS descriptors in mammography reports.
SplitAVG: A heterogeneity-aware federated deep learning method for medical imaging
In this study, we propose a novel heterogeneity-aware federated learning method, SplitAVG, to overcome the performance drops from data heterogeneity in federated learning.
COVID-19 Lung Lesion Segmentation Using a Sparsely Supervised Mask R-CNN on Chest X-rays Automatically Computed from Volumetric CTs
Chest X-rays of coronavirus disease 2019 (COVID-19) patients are frequently obtained to determine the extent of lung disease and are a valuable source of data for creating artificial intelligence models.
Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis
Automated seizure detection and classification from electroencephalography (EEG) can greatly improve seizure diagnosis and treatment.
Addressing catastrophic forgetting for medical domain expansion
Notably, this approach degrades model performance at the original institution, a phenomenon known as catastrophic forgetting.
Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation
Although various methods such as domain adaptation and domain generalization have evolved to combat this challenge, learning robust and generalizable representations is core to medical image understanding, and continues to be a problem.
Data Valuation for Medical Imaging Using Shapley Value: Application on A Large-scale Chest X-ray Dataset
In this study, we used data Shapley, a data valuation metric, to quantify the value of training data to the performance of a pneumonia detection algorithm in a large chest X-ray dataset.
Probabilistic bounds on neuron death in deep rectifier networks
Neuron death is a complex phenomenon with implications for model trainability: the deeper the network, the lower the probability of finding a valid initialization.
Plexus Convolutional Neural Network (PlexusNet): A novel neural network architecture for histologic image analysis
A well-fitted PlexusNet-based model delivered comparable classification performance (AUC: 0. 963) in distinguishing prostate cancer from healthy tissues, although it was at least 23 times smaller, had a better model calibration and clinical utility than the comparison models.
Self-Attention Capsule Networks for Object Classification
Then, the Self-Attention layer learns to suppress irrelevant regions based on features analysis and highlights salient features useful for a specific task.
Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI
For an optimal probability threshold, detection and segmentation performance was assessed on a per metastasis basis.
CT organ segmentation using GPU data augmentation, unsupervised labels and IOU loss
To achieve the best results from data augmentation, our model uses the intersection-over-union (IOU) loss function, a close relative of the Dice loss.
A Scalable Machine Learning Approach for Inferring Probabilistic US-LI-RADS Categorization
We propose a scalable computerized approach for large-scale inference of Liver Imaging Reporting and Data System (LI-RADS) final assessment categories in narrative ultrasound (US) reports.
Abstract: Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients
We propose a deep learning model - Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) for estimating short-term life expectancy (3 months) of the patients by analyzing free-text clinical notes in the electronic medical record, while maintaining the temporal visit sequence.
Intelligent Word Embeddings of Free-Text Radiology Reports
Radiology reports are a rich resource for advancing deep learning applications in medicine by leveraging the large volume of data continuously being updated, integrated, and shared.
Institutionally Distributed Deep Learning Networks
In such cases, sharing a deep learning model is a more attractive alternative.
Inferring Generative Model Structure with Static Analysis
Obtaining enough labeled data to robustly train complex discriminative models is a major bottleneck in the machine learning pipeline.
A Fully-Automated Pipeline for Detection and Segmentation of Liver Lesions and Pathological Lymph Nodes
We propose a fully-automated method for accurate and robust detection and segmentation of potentially cancerous lesions found in the liver and in lymph nodes.
Computerized Multiparametric MR image Analysis for Prostate Cancer Aggressiveness-Assessment
We propose an automated method for detecting aggressive prostate cancer(CaP) (Gleason score >=7) based on a comprehensive analysis of the lesion and the surrounding normal prostate tissue which has been simultaneously captured in T2-weighted MR images, diffusion-weighted images (DWI) and apparent diffusion coefficient maps (ADC).
Piecewise convexity of artificial neural networks
Finally, that the network as a function of all its parameters is piecewise multi-convex, a generalization of biconvexity.
Adaptive Local Window for Level Set Segmentation of CT and MRI Liver Lesions
We compare our method to a global level set segmentation and to local framework that uses predefined fixed-size square windows.
