Search Results for author:
Found 13 papers, 3 papers with code
A SAM-guided and Match-based Semi-Supervised Segmentation Framework for Medical Imaging
This study introduces SAMatch, a SAM-guided Match-based framework for semi-supervised medical image segmentation, aimed at improving pseudo label quality in data-scarce scenarios.
A Gaussian Process Based Method with Deep Kernel Learning for Pricing High-dimensional American Options
In this work, we present a novel machine learning approach for pricing high-dimensional American options based on the modified Gaussian process regression (GPR).
An Efficient 1 Iteration Learning Algorithm for Gaussian Mixture Model And Gaussian Mixture Embedding For Neural Network
We propose an Gaussian Mixture Model (GMM) learning algorithm, based on our previous work of GMM expansion idea.
Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancers
For Head and Neck Cancers (HNC) patient management, automatic gross tumor volume (GTV) segmentation and accurate pre-treatment cancer recurrence prediction are of great importance to assist physicians in designing personalized management plans, which have the potential to improve the treatment outcome and quality of life for HNC patients.
Leveraging Global Binary Masks for Structure Segmentation in Medical Images
We proposed to leverage the consistency of organs' anatomical shape and position information in medical images.
Saliency-Guided Deep Learning Network for Automatic Tumor Bed Volume Delineation in Post-operative Breast Irradiation
The saliency maps were generated by identifying markers on CT images.
Breast Ultrasound Computer-Aided Diagnosis Using Structure-Aware Triplet Path Networks
These findings indicated that SATPN is promising for effective breast US lesion CAD using small datasets.
BIRADS Features-Oriented Semi-supervised Deep Learning for Breast Ultrasound Computer-Aided Diagnosis
Then, the converted BFMs are used as the input of an SDL network, which performs unsupervised stacked convolutional auto-encoder (SCAE) image reconstruction guided by lesion classification.
Medical Physics
Three-Dimensional Dose Prediction for Lung IMRT Patients with Deep Neural Networks: Robust Learning from Heterogeneous Beam Configurations
However, the existing methods only use patient anatomy as input and assume consistent beam configuration for all patients in the training database.
Towards automated patient data cleaning using deep learning: A feasibility study on the standardization of organ labeling
Organs are often labeled inconsistently at different institutions (sometimes even within the same institution) and at different time periods, which poses a problem for clinical research, especially for multi-institutional collaborative clinical research where the acquired patient data is not being used effectively.
Accurate Real Time Localization Tracking in A Clinical Environment using Bluetooth Low Energy and Deep Learning
Deep learning has started to revolutionize several different industries, and the applications of these methods in medicine are now becoming more commonplace.
A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning
With the advancement of treatment modalities in radiation therapy for cancer patients, outcomes have improved, but at the cost of increased treatment plan complexity and planning time.
