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Found 20 papers, 7 papers with code
Thyroid ultrasound diagnosis improvement via multi-view self-supervised learning and two-stage pre-training
In this study, we proposed a multi-view contrastive self-supervised method to improve thyroid nodule classification and segmentation performance with limited manual labels.
MUVF-YOLOX: A Multi-modal Ultrasound Video Fusion Network for Renal Tumor Diagnosis
In addition, we design an object-level temporal aggregation (OTA) module that can automatically filter low-quality features and efficiently integrate temporal information from multiple frames to improve the accuracy of tumor diagnosis.
Inflated 3D Convolution-Transformer for Weakly-supervised Carotid Stenosis Grading with Ultrasound Videos
First, to avoid the requirement of laborious and unreliable annotation, we propose a novel and effective video classification network for weakly-supervised CSG.
Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis
Our main contributions include: 1) we present the first work that can synthesize realistic B-mode US images with high-resolution and customized texture editing features; 2) to enhance structural details of generated images, we propose to introduce auxiliary sketch guidance into a conditional GAN.
Steerable Pyramid Transform Enables Robust Left Ventricle Quantification
First, the basis functions of SPT match the anatomical structure of the LV as well as the geometric characteristics of the estimated indices.
AWSnet: An Auto-weighted Supervision Attention Network for Myocardial Scar and Edema Segmentation in Multi-sequence Cardiac Magnetic Resonance Images
Extensive experimental results on a publicly available dataset from Myocardial pathology segmentation combining multi-sequence CMR (MyoPS 2020) demonstrate our method can achieve promising performance compared with other state-of-the-art methods.
Bootstrap Representation Learning for Segmentation on Medical Volumes and Sequences
Based on the continuity between slices/frames and the common spatial layout of organs across volumes/sequences, we introduced a novel bootstrap self-supervised representation learning method by leveraging the predictable possibility of neighboring slices.
Joint Landmark and Structure Learning for Automatic Evaluation of Developmental Dysplasia of the Hip
In this study, we propose a multi-task framework to learn the relationships among landmarks and structures jointly and automatically evaluate DDH.
Agent with Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D Ultrasound
2D US has to perform scanning for each SP, which is time-consuming and operator-dependent.
Style-invariant Cardiac Image Segmentation with Test-time Augmentation
Based on the zero-shot style transfer to remove appearance shift and test-time augmentation to explore diverse underlying anatomy, our proposed method is effective in combating the appearance shift.
FetusMap: Fetal Pose Estimation in 3D Ultrasound
(i) This is the first work about 3D pose estimation of fetus in the literature.
Agent with Warm Start and Active Termination for Plane Localization in 3D Ultrasound
In this study, we propose a novel reinforcement learning (RL) framework to automatically localize fetal brain standard planes in 3D US.
Segmentation of Multimodal Myocardial Images Using Shape-Transfer GAN
Myocardium segmentation of late gadolinium enhancement (LGE) Cardiac MR images is important for evaluation of infarction regions in clinical practice.
Cardiac Motion Scoring with Segment- and Subject-level Non-Local Modeling
Experiments on 1440 myocardium segments of 90 subjects from short axis MR sequences of multiple lengths prove that Cardiac-MOS achieves reliable performance, with correlation of 0. 926 for motion score index estimation and accuracy of 77. 4\% for motion scoring.
Full Quantification of Left Ventricle via Deep Multitask Learning Network Respecting Intra- and Inter-Task Relatedness
Cardiac left ventricle (LV) quantification is among the most clinically important tasks for identification and diagnosis of cardiac diseases, yet still a challenge due to the high variability of cardiac structure and the complexity of temporal dynamics.
Direct Estimation of Regional Wall Thicknesses via Residual Recurrent Neural Network
Accurate estimation of regional wall thicknesses (RWT) of left ventricular (LV) myocardium from cardiac MR sequences is of significant importance for identification and diagnosis of cardiac disease.
Direct Multitype Cardiac Indices Estimation via Joint Representation and Regression Learning
However, estimation of multitype cardiac indices with consistently reliable and high accuracy is still a great challenge due to the high variability of cardiac structures and complexity of temporal dynamics in cardiac MR sequences.
Learn to Evaluate Image Perceptual Quality Blindly from Statistics of Self-similarity
Among the various image quality assessment (IQA) tasks, blind IQA (BIQA) is particularly challenging due to the absence of knowledge about the reference image and distortion type.
Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index
We present a new effective and efficient IQA model, called gradient magnitude similarity deviation (GMSD).
Ranked #6 on
Image Quality Assessment
on MSU FR VQA Database
Learning without Human Scores for Blind Image Quality Assessment
An interesting question is then: can we learn for effective BIQA without using human scored images?
