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Found 11 papers, 2 papers with code
A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge.
A Novel Autoencoders-LSTM Model for Stroke Outcome Prediction using Multimodal MRI Data
Patient outcome prediction is critical in management of ischemic stroke.
DeepFEL: Deep Fastfood Ensemble Learning for Histopathology Image Analysis
Computational pathology tasks have some unique characterises such as multi-gigapixel images, tedious and frequently uncertain annotations, and unavailability of large number of cases [13].
CNN-LSTM Based Multimodal MRI and Clinical Data Fusion for Predicting Functional Outcome in Stroke Patients
For each MR image module, a dedicated network provides preliminary prediction of the clinical outcome using the modified Rankin scale (mRS).
Deep Multi-Resolution Dictionary Learning for Histopathology Image Analysis
In this paper, we propose a deep dictionary learning approach to solve the problem of tissue phenotyping in histology images.
Magnetic Resonance Spectroscopy Quantification using Deep Learning
Magnetic resonance spectroscopy (MRS) is an important technique in biomedical research and it has the unique capability to give a non-invasive access to the biochemical content (metabolites) of scanned organs.
Bag of Recurrence Patterns Representation for Time-Series Classification
Time-Series Classification (TSC) has attracted a lot of attention in pattern recognition, because wide range of applications from different domains such as finance and health informatics deal with time-series signals.
Classification of Time-Series Images Using Deep Convolutional Neural Networks
While the majority of Time-Series Classification (TSC) literature is focused on 1D signals, this paper uses Recurrence Plots (RP) to transform time-series into 2D texture images and then take advantage of the deep CNN classifier.
Automatic Identification of Retinal Arteries and Veins in Fundus Images using Local Binary Patterns
Artery and vein (AV) classification of retinal images is a key to necessary tasks, such as automated measurement of arteriolar-to-venular diameter ratio (AVR).
Classifiers With a Reject Option for Early Time-Series Classification
Early classification of time-series data in a dynamic environment is a challenging problem of great importance in signal processing.
ECOC-Based Training of Neural Networks for Face Recognition
Error Correcting Output Codes, ECOC, is an output representation method capable of discovering some of the errors produced in classification tasks.
