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Found 11 papers, 1 papers with code
Breast Cancer Histopathology Image based Gene Expression Prediction using Spatial Transcriptomics data and Deep Learning
Here we present BrST-Net, a deep learning framework for predicting gene expression from histopathology images using spatial transcriptomics data.
Hybrid Dual Mean-Teacher Network With Double-Uncertainty Guidance for Semi-Supervised Segmentation of MRI Scans
We then propose a hybrid regularization module to encourage both student models to produce results close to the uncertainty-weighted hybrid prediction.
IKD+: Reliable Low Complexity Deep Models For Retinopathy Classification
Deep neural network (DNN) models for retinopathy have estimated predictive accuracies in the mid-to-high 90%.
Understanding metric-related pitfalls in image analysis validation
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice.
Fully Elman Neural Network: A Novel Deep Recurrent Neural Network Optimized by an Improved Harris Hawks Algorithm for Classification of Pulmonary Arterial Wedge Pressure
The proposed CNN-FENN-HHO+ method outperforms the proposed CNN-MLP, CNN-ENN and CNN-FENN methods and improved the classification performance metrics across 5-fold cross-validation.
From Nano to Macro: Overview of the IEEE Bio Image and Signal Processing Technical Committee
The Bio Image and Signal Processing (BISP) Technical Committee (TC) of the IEEE Signal Processing Society (SPS) promotes activities within the broad technical field of biomedical image and signal processing.
MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentation
Image segmentation is an important task in medical imaging.
Neural Network Compression by Joint Sparsity Promotion and Redundancy Reduction
Compression of convolutional neural network models has recently been dominated by pruning approaches.
Metrics reloaded: Pitfalls and recommendations for image analysis validation
The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output.
Leveraging Image Complexity in Macro-Level Neural Network Design for Medical Image Segmentation
Two common examples are downsampling of the input images and reducing the network depth to meet computer memory constraints.
Common Limitations of Image Processing Metrics: A Picture Story
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.
