Search Results for author:
Found 8 papers, 3 papers with code
Unsupervised Iterative U-Net with an Internal Guidance Layer for Vertebrae Contrast Enhancement in Chest X-Ray Images
This innovative approach has the potential to significantly enhance the diagnostic accuracy and early detection of diseases, making it a promising advancement in X-ray imaging technology.
Improving Gradient-Trend Identification: Fast-Adaptive Moment Estimation with Finance-Inspired Triple Exponential Moving Average
Existing optimizers predominantly adopt techniques based on the first-order exponential moving average (EMA), which results in noticeable delays that impede the real-time tracking of gradients trend and consequently yield sub-optimal performance.
Deep Active Lesion Segmentation
Lesion segmentation is an important problem in computer-assisted diagnosis that remains challenging due to the prevalence of low contrast, irregular boundaries that are unamenable to shape priors.
From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI
Unfortunately, acquiring sufficient "labeled" pairs of {Image, fMRI} (i. e., images with their corresponding fMRI responses) to span the huge space of natural images is prohibitive for many reasons.
Self-Attention Capsule Networks for Object Classification
Then, the Self-Attention layer learns to suppress irrelevant regions based on features analysis and highlights salient features useful for a specific task.
TVAE: Triplet-Based Variational Autoencoder using Metric Learning
Deep metric learning has been demonstrated to be highly effective in learning semantic representation and encoding information that can be used to measure data similarity, by relying on the embedding learned from metric learning.
A Fully-Automated Pipeline for Detection and Segmentation of Liver Lesions and Pathological Lymph Nodes
We propose a fully-automated method for accurate and robust detection and segmentation of potentially cancerous lesions found in the liver and in lymph nodes.
Adaptive Local Window for Level Set Segmentation of CT and MRI Liver Lesions
We compare our method to a global level set segmentation and to local framework that uses predefined fixed-size square windows.
