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Found 18 papers, 4 papers with code
Image-Based Soil Organic Carbon Remote Sensing from Satellite Images with Fourier Neural Operator and Structural Similarity
To study the use of CNNs on SOC remote sensing, here we propose the FNO-DenseNet based on the Fourier neural operator (FNO).
FNOSeg3D: Resolution-Robust 3D Image Segmentation with Fourier Neural Operator
Due to the computational complexity of 3D medical image segmentation, training with downsampled images is a common remedy for out-of-memory errors in deep learning.
HartleyMHA: Self-Attention in Frequency Domain for Resolution-Robust and Parameter-Efficient 3D Image Segmentation
With the introduction of Transformers, different attention-based models have been proposed for image segmentation with promising results.
3D Segmentation with Fully Trainable Gabor Kernels and Pearson's Correlation Coefficient
For existing loss functions for multi-class image segmentation, there is usually a tradeoff among accuracy, robustness to hyperparameters, and manual weight selections for combining different losses.
Addressing Deep Learning Model Uncertainty in Long-Range Climate Forecasting with Late Fusion
Global warming leads to the increase in frequency and intensity of climate extremes that cause tremendous loss of lives and property.
Basis Scaling and Double Pruning for Efficient Inference in Network-Based Transfer Learning
Network-based transfer learning allows the reuse of deep learning features with limited data, but the resulting models can be unnecessarily large.
Multiview and Multiclass Image Segmentation using Deep Learning in Fetal Echocardiography
Congenital heart disease (CHD) is the most common congenital abnormality associated with birth defects in the United States.
Channel Scaling: A Scale-and-Select Approach for Transfer Learning
By imposing L1 regularization and thresholding on the scaling weights, this framework iteratively removes unnecessary feature channels from a pre-trained model.
Looking in the Right place for Anomalies: Explainable AI through Automatic Location Learning
Deep learning has now become the de facto approach to the recognition of anomalies in medical imaging.
Chest X-ray Report Generation through Fine-Grained Label Learning
Obtaining automated preliminary read reports for common exams such as chest X-rays will expedite clinical workflows and improve operational efficiencies in hospitals.
SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation
Deep learning has largely reduced the need for manual feature selection in image segmentation.
Automated Detection and Type Classification of Central Venous Catheters in Chest X-Rays
Central venous catheters (CVCs) are commonly used in critical care settings for monitoring body functions and administering medications.
Boosting the rule-out accuracy of deep disease detection using class weight modifiers
We experiment with two different deep neural network architectures and show that the proposed method results in a large improvement in the performance of the classifiers, specially on negated findings.
Building a Benchmark Dataset and Classifiers for Sentence-Level Findings in AP Chest X-rays
Chest X-rays are the most common diagnostic exams in emergency rooms and hospitals.
Identifying disease-free chest X-ray images with deep transfer learning
In this work, we report a deep neural network trained for classifying CXRs with the goal of identifying a large number of normal (disease-free) images without risking the discharge of sick patients.
3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes
In this paper, we propose a network architecture and the corresponding loss function which improve segmentation of very small structures.
Building medical image classifiers with very limited data using segmentation networks
Deep learning has shown promising results in medical image analysis, however, the lack of very large annotated datasets confines its full potential.
Building Disease Detection Algorithms with Very Small Numbers of Positive Samples
We train a discriminative segmentation model only on normal images to provide a source of knowledge to be transferred to a disease detection classifier.
