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Found 5 papers, 2 papers with code
Push the Boundary of SAM: A Pseudo-label Correction Framework for Medical Segmentation
One key advantage of our model is its ability to train deep networks using SAM-generated pseudo labels without relying on a set of expert-level annotations while attaining good segmentation performance.
Cardiac Adipose Tissue Segmentation via Image-Level Annotations
To lessen the need for pixel-wise labeling, we develop a two-stage deep learning framework for cardiac adipose tissue segmentation using image-level annotations on OCT images of human cardiac substrates.
Recursive Refinement Network for Deformable Lung Registration between Exhale and Inhale CT Scans
We introduce a recursive refinement network (RRN) for unsupervised medical image registration, to extract multi-scale features, construct normalized local cost correlation volume and recursively refine volumetric deformation vector fields.
Ranked #1 on
Image Registration
on DIR-LAB COPDgene
PTNet: A High-Resolution Infant MRI Synthesizer Based on Transformer
Magnetic resonance imaging (MRI) noninvasively provides critical information about how human brain structures develop across stages of life.
Generative Adversarial Network
Vocal Bursts Intensity Prediction
Co-Seg: An Image Segmentation Framework Against Label Corruption
Supervised deep learning performance is heavily tied to the availability of high-quality labels for training.
