research
∙
03/11/2022
ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRI
Deep artificial neural networks (DNNs) have moved to the forefront of me...
share
research
∙
03/09/2022
Metastatic Cancer Outcome Prediction with Injective Multiple Instance Pooling
Cancer stage is a large determinant of patient prognosis and management ...
research
∙
10/14/2021
BI-RADS BERT Using Section Tokenization to Understand Radiology Reports
Radiology reports are the main form of communication between radiologist...
research
∙
08/16/2021
Improving Self-supervised Learning with Hardness-aware Dynamic Curriculum Learning: An Application to Digital Pathology
Self-supervised learning (SSL) has recently shown tremendous potential t...
research
∙
02/07/2021
Self-supervised driven consistency training for annotation efficient histopathology image analysis
Training a neural network with a large labeled dataset is still a domina...
research
∙
12/12/2020
AMINN: Autoencoder-based Multiple Instance Neural Network for Outcome Prediction of Multifocal Liver Metastases
Colorectal cancer is one of the most common and lethal cancers and color...
research
∙
12/01/2020
Overcoming the limitations of patch-based learning to detect cancer in whole slide images
Whole slide images (WSIs) pose unique challenges when training deep lear...
research
∙
11/27/2020
Self supervised contrastive learning for digital histopathology
Unsupervised learning has been a long-standing goal of machine learning ...
research
∙
12/28/2019
Learning to segment images with classification labels
Two of the most common tasks in medical imaging are classification and s...
research
∙
12/28/2019
Deep neural network models for computational histopathology: A survey
Histopathological images contain rich phenotypic information that can be...
research
∙
10/09/2019
BIAS: Transparent reporting of biomedical image analysis challenges
The number of biomedical image analysis challenges organized per year is...
research
∙
09/06/2019
Unsupervised Clustering of Quantitative Imaging Phenotypes using Autoencoder and Gaussian Mixture Model
Quantitative medical image computing (radiomics) has been widely applied...
research
∙
09/05/2019
Intensity augmentation for domain transfer of whole breast segmentation in MRI
The segmentation of the breast from the chest wall is an important first...
research
∙
11/28/2018
Cluster-Based Learning from Weakly Labeled Bags in Digital Pathology
To alleviate the burden of gathering detailed expert annotations when tr...
research
∙
06/27/2016