research
∙
06/28/2023
A systematic study of the foreground-background imbalance problem in deep learning for object detection
The class imbalance problem in deep learning has been explored in severa...
share
research
∙
05/11/2023
Convolutional Neural Networks Rarely Learn Shape for Semantic Segmentation
Shape learning, or the ability to leverage shape information, could be a...
research
∙
05/04/2023
Unsupervised anomaly localization in high-resolution breast scans using deep pluralistic image completion
Automated tumor detection in Digital Breast Tomosynthesis (DBT) is a dif...
research
∙
04/20/2023
Segment Anything Model for Medical Image Analysis: an Experimental Study
Training segmentation models for medical images continues to be challeng...
research
∙
03/13/2023
SuperMask: Generating High-resolution object masks from multi-view, unaligned low-resolution MRIs
Three-dimensional segmentation in magnetic resonance images (MRI), which...
research
∙
03/08/2023
Reverse Engineering Breast MRIs: Predicting Acquisition Parameters Directly from Images
The image acquisition parameters (IAPs) used to create MRI scans are cen...
research
∙
07/27/2022
Deep Learning for Classification of Thyroid Nodules on Ultrasound: Validation on an Independent Dataset
Objectives: The purpose is to apply a previously validated deep learning...
research
∙
07/25/2022
Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists
A fully-automated deep learning algorithm matched performance of radiolo...
research
∙
07/06/2022
The Intrinsic Manifolds of Radiological Images and their Role in Deep Learning
The manifold hypothesis is a core mechanism behind the success of deep l...
research
∙
03/16/2022
Automated Grading of Radiographic Knee Osteoarthritis Severity Combined with Joint Space Narrowing
The assessment of knee osteoarthritis (KOA) severity on knee X-rays is a...
research
∙
11/22/2021
REPLICA: Enhanced Feature Pyramid Network by Local Image Translation and Conjunct Attention for High-Resolution Breast Tumor Detection
We introduce an improvement to the feature pyramid network of standard o...
research
∙
04/27/2021
Do We Expect More from Radiology AI than from Radiologists?
What we expect from radiology AI algorithms will shape the selection and...
research
∙
02/05/2021
Multi-Label Annotation of Chest Abdomen Pelvis Computed Tomography Text Reports Using Deep Learning
To develop a high throughput multi-label annotator for body Computed Tom...
research
∙
11/13/2020
Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model
Breast cancer screening is one of the most common radiological tasks wit...
research
∙
10/31/2020
Weakly Supervised 3D Classification of Chest CT using Aggregated Multi-Resolution Deep Segmentation Features
Weakly supervised disease classification of CT imaging suffers from poor...
research
∙
08/03/2020
Weakly Supervised Multi-Organ Multi-Disease Classification of Body CT Scans
We designed a multi-organ, multi-label disease classification algorithm ...
research
∙
02/12/2020
Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes
Developing machine learning models for radiology requires large-scale im...
research
∙
12/16/2019
Normalization of breast MRIs using Cycle-Consistent Generative Adversarial Networks
Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is widely...
research
∙
06/09/2019
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
Recent analysis identified distinct genomic subtypes of lower-grade glio...
research
∙
07/05/2018
Automatic deep learning-based normalization of breast dynamic contrast-enhanced magnetic resonance images
Objective: To develop an automatic image normalization algorithm for int...
research
∙
02/10/2018
Deep learning in radiology: an overview of the concepts and a survey of the state of the art
Deep learning is a branch of artificial intelligence where networks of s...
research
∙
11/29/2017
Deep Learning for identifying radiogenomic associations in breast cancer
Purpose: To determine whether deep learning models can distinguish betwe...
research
∙
11/28/2017
Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ
Purpose: To determine whether deep learning-based algorithms applied to ...
research
∙
10/15/2017