Search Results for author:
Found 28 papers, 9 papers with code
QIAI at MEDIQA 2021: Multimodal Radiology Report Summarization
This paper describes the solution of the QIAI lab sent to the Radiology Report Summarization (RRS) challenge at MEDIQA 2021.
Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays
Thus, it is unknown whether common augmentation strategies employed in Siamese representation learning generalize to medical images and to what extent.
ATCON: Attention Consistency for Vision Models
We propose an unsupervised fine-tuning method that optimizes the consistency of attention maps and show that it improves both classification performance and the quality of attention maps.
Contrastive learning-based pretraining improves representation and transferability of diabetic retinopathy classification models
CL based pretraining with NST significantly improves DL classification performance, helps the model generalize well (transferable from EyePACS to UIC data), and allows training with small, annotated datasets, therefore reducing ground truth annotation burden of the clinicians.
The Importance of Background Information for Out of Distribution Generalization
Domain generalization in medical image classification is an important problem for trustworthy machine learning to be deployed in healthcare.
Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging
The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder data sharing.
Masked Co-attentional Transformer reconstructs 100x ultra-fast/low-dose whole-body PET from longitudinal images and anatomically guided MRI
In this study, we utilize the global similarity between baseline and follow-up PET and magnetic resonance (MR) images to develop Masked-LMCTrans, a longitudinal multi-modality co-attentional CNN-Transformer that provides interaction and joint reasoning between serial PET/MRs of the same patient.
Multimodal spatiotemporal graph neural networks for improved prediction of 30-day all-cause hospital readmission
Measures to predict 30-day readmission are considered an important quality factor for hospitals as accurate predictions can reduce the overall cost of care by identifying high risk patients before they are discharged.
Automated Detection of Patients in Hospital Video Recordings
We evaluate an ImageNet pre-trained Mask R-CNN, a standard deep learning model for object detection, on the task of patient detection using our own curated dataset of 45 videos of hospital patients.
RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR
Despite the routine use of electronic health record (EHR) data by radiologists to contextualize clinical history and inform image interpretation, the majority of deep learning architectures for medical imaging are unimodal, i. e., they only learn features from pixel-level information.
Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence
Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses.
TIME-LAPSE: Learning to say “I don't know” through spatio-temporal uncertainty scoring
We show that TIME-LAPSE is more driven by semantic content compared to other methods, i. e., it is more robust to dataset statistics.
Handling Data Heterogeneity with Generative Replay in Collaborative Learning for Medical Imaging
Specifically, instead of directly training a model for task performance, we develop a novel dual model architecture: a primary model learns the desired task, and an auxiliary "generative replay model" allows aggregating knowledge from the heterogenous clients.
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning
Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data private at each institution.
Double Descent Optimization Pattern and Aliasing: Caveats of Noisy Labels
Optimization plays a key role in the training of deep neural networks.
Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video Processing
We propose a semi-supervised machine learning training strategy to improve event detection performance on sequential data, such as video recordings, when only sparse labels are available, such as event start times without their corresponding end times.
Federated Learning for Breast Density Classification: A Real-World Implementation
Building robust deep learning-based models requires large quantities of diverse training data.
Random Bundle: Brain Metastases Segmentation Ensembling through Annotation Randomization
We introduce a novel ensembling method, Random Bundle (RB), that improves performance for brain metastases segmentation.
Brain Metastasis Segmentation Network Trained with Robustness to Annotations with Multiple False Negatives
Even with a simulated false negative rate as high as 50%, applying our loss function to randomly censored data preserves maximum sensitivity at 97% of the baseline with uncensored training data, compared to just 10% for a standard loss function.
Handling Missing MRI Input Data in Deep Learning Segmentation of Brain Metastases: A Multi-Center Study
A deep learning based segmentation model for automatic segmentation of brain metastases, named DropOut, was trained on multi-sequence MRI from 100 patients, and validated/tested on 10/55 patients.
MRI Pulse Sequence Integration for Deep-Learning Based Brain Metastasis Segmentation
We illustrate not only the generalizability of the network but also the utility of this robustness when applying the trained model to data from a different center, which does not use the same pulse sequences.
Deep Learning for Prostate Pathology
For case usage, these models were applied for the annotation tasks in clinician-oriented pathology reports for prostatectomy specimens.
Deep Active Lesion Segmentation
Lesion segmentation is an important problem in computer-assisted diagnosis that remains challenging due to the prevalence of low contrast, irregular boundaries that are unamenable to shape priors.
Cross-Modal Data Programming Enables Rapid Medical Machine Learning
Labeling training datasets has become a key barrier to building medical machine learning models.
Improving Sample Complexity with Observational Supervision
Supervised machine learning models for high-value computer vision applications such as medical image classification often require large datasets labeled by domain experts, which are slow to collect, expensive to maintain, and static with respect to changes in the data distribution.
A Deep-learning Approach for Prognosis of Age-Related Macular Degeneration Disease using SD-OCT Imaging Biomarkers
We propose a hybrid sequential deep learning model to predict the risk of AMD progression in non-exudative AMD eyes at multiple timepoints, starting from short-term progression (3-months) up to long-term progression (21-months).
TVAE: Triplet-Based Variational Autoencoder using Metric Learning
Deep metric learning has been demonstrated to be highly effective in learning semantic representation and encoding information that can be used to measure data similarity, by relying on the embedding learned from metric learning.
Optimizing and Visualizing Deep Learning for Benign/Malignant Classification in Breast Tumors
Breast cancer has the highest incidence and second highest mortality rate for women in the US.
