Search Results for author:
Found 7 papers, 2 papers with code
The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI
Gliomas are the most common malignant primary brain tumors in adults and one of the deadliest types of cancer.
Deep learning automates Cobb angle measurement compared with multi-expert observers
To address these challenges and the lack of interpretability found in certain existing automated methods, we have created fully automated software that not only precisely measures the Cobb angle but also provides clear visualizations of these measurements.
SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI
In our study, we propose a versatile, publicly available deep-learning model for bone segmentation in MRI across multiple standard MRI locations.
Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach
The final image classification model, trained using both manually labeled and pseudo-labeled data, had the higher weighted average AUC (WAUC: 0. 903) value and higher AUC-ROC values among all classes (normal AUC-ROC: 0. 894; abnormal AUC-ROC: 0. 896, arthroplasty AUC-ROC: 0. 990) compared to the baseline model (WAUC=0. 857; normal AUC-ROC: 0. 842; abnormal AUC-ROC: 0. 848, arthroplasty AUC-ROC: 0. 987), trained using only manually labeled data.
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 algorithm to a new thyroid nodule ultrasound image dataset and compare its performances with radiologists.
Multistep Automated Data Labelling Procedure (MADLaP) for Thyroid Nodules on Ultrasound: An Artificial Intelligence Approach for Automating Image Annotation
Machine learning (ML) for diagnosis of thyroid nodules on ultrasound is an active area of research.
Optical Character Recognition
Optical Character Recognition (OCR)
Knee arthritis severity measurement using deep learning: a publicly available algorithm with a multi-institutional validation showing radiologist-level performance
We propose a novel deep learning-based five-step algorithm to automatically grade KOA from posterior-anterior (PA) views of radiographs: (1) image preprocessing (2) localization of knees joints in the image using the YOLO v3-Tiny model, (3) initial assessment of the severity of osteoarthritis using a convolutional neural network-based classifier, (4) segmentation of the joints and calculation of the joint space narrowing (JSN), and (5), a combination of the JSN and the initial assessment to determine a final Kellgren-Lawrence (KL) score.
