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Found 10 papers, 4 papers with code
ResNet Structure Simplification with the Convolutional Kernel Redundancy Measure
Deep learning, especially convolutional neural networks, has triggered accelerated advancements in computer vision, bringing changes into our daily practice.
The Open Kidney Ultrasound Data Set
This data set is a high-quality data set, including two sets of expert annotations, with a larger breadth of images than previously available.
The Kidneys Are Not All Normal: Investigating the Speckle Distributions of Transplanted Kidneys
Furthermore, it is unclear how these distributions vary by patient variables such as age, sex, body mass index, primary disease, or donor type.
Multi-task UNet: Jointly Boosting Saliency Prediction and Disease Classification on Chest X-ray Images
To support the use of visual attention, this paper describes a novel deep learning model for visual saliency prediction on chest X-ray (CXR) images.
Gaze-Guided Class Activation Mapping: Leveraging Human Attention for Network Attention in Chest X-rays Classification
However, no previous research has combined the network attention and human attention.
Multifrequency 3D Elasticity Reconstruction withStructured Sparsity and ADMM
We evaluate our new method in multiple in silico and phantom experiments, with comparisons with existing methods, and we show improvements in contrast to noise and signal to noise ratios.
Echo-SyncNet: Self-supervised Cardiac View Synchronization in Echocardiography
In echocardiography (echo), an electrocardiogram (ECG) is conventionally used to temporally align different cardiac views for assessing critical measurements.
U-LanD: Uncertainty-Driven Video Landmark Detection
We tackle a specifically challenging problem, where training labels are noisy and highly sparse.
On Modelling Label Uncertainty in Deep Neural Networks: Automatic Estimation of Intra-observer Variability in 2D Echocardiography Quality Assessment
Nevertheless, the observer variability in the expert's assessment can impact the quality quantification accuracy.
Deep Neural Maps
We introduce a new unsupervised representation learning and visualization using deep convolutional networks and self organizing maps called Deep Neural Maps (DNM).
