Search Results for author:
Found 14 papers, 3 papers with code
Scout-Net: Prospective Personalized Estimation of CT Organ Doses from Scout Views
To optimize CT acquisitions before scanning, rapid prediction of patient-specific organ dose is needed prospectively, using available scout images.
Forward-Forward Contrastive Learning
Medical image classification is one of the most important tasks for computer-aided diagnosis.
Semi-Supervised Relational Contrastive Learning
Disease diagnosis from medical images via supervised learning is usually dependent on tedious, error-prone, and costly image labeling by medical experts.
Single Image Internal Distribution Measurement Using Non-Local Variational Autoencoder
To harvest maximum details for various receptive regions and high-quality synthetic images, \texttt{NLVAE} is introduced as a self-supervised strategy that reconstructs high-resolution images using disentangled information from the non-local neighbourhood.
Generalized Multi-Task Learning from Substantially Unlabeled Multi-Source Medical Image Data
Semi-supervised learning from limited quantities of labeled data has shown promise as an alternative.
Window-Level is a Strong Denoising Surrogate
However, those approaches require access to large training sets, specifically the full dose CT images for reference, which can often be difficult to obtain.
MultiMix: Sparingly Supervised, Extreme Multitask Learning From Medical Images
Our extensive experimentation with varied quantities of labeled data in the training sets justify the effectiveness of our multitasking model for the classification of pneumonia and segmentation of lungs from chest X-ray images.
Bipartite Distance for Shape-Aware Landmark Detection in Spinal X-Ray Images
Scoliosis is a congenital disease that causes lateral curvature in the spine.
Progressive Adversarial Semantic Segmentation
Medical image computing has advanced rapidly with the advent of deep learning techniques such as convolutional neural networks.
Partly Supervised Multitask Learning
Leveraging self-supervision and adversarial training, we propose a novel general purpose semi-supervised, multiple-task model---namely, self-supervised, semi-supervised, multitask learning (S$^4$MTL)---for accomplishing two important tasks in medical imaging, segmentation and diagnostic classification.
Analysis of Scoliosis From Spinal X-Ray Images
Leveraging a carefully-adjusted U-Net model with progressive side outputs, we propose an end-to-end segmentation model that provides a fully automatic and reliable segmentation of the vertebrae associated with scoliosis measurement.
Semi-Supervised Multi-Task Learning With Chest X-Ray Images
Discriminative models that require full supervision are inefficacious in the medical imaging domain when large labeled datasets are unavailable.
Multi-Adversarial Variational Autoencoder Networks
The unsupervised training of GANs and VAEs has enabled them to generate realistic images mimicking real-world distributions and perform image-based unsupervised clustering or semi-supervised classification.
Automatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network
We evaluated our model using 84 chest CT scans from the LIDC and 154 pathological cases from the LTRC datasets.
