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Found 6 papers, 3 papers with code
SegReg: Segmenting OARs by Registering MR Images and CT Annotations
This manual process is highly time-consuming and expensive, limiting the number of patients who can receive timely radiotherapy.
Multi-Head Multi-Loss Model Calibration
Delivering meaningful uncertainty estimates is essential for a successful deployment of machine learning models in the clinical practice.
Mutual information neural estimation for unsupervised multi-modal registration of brain images
We propose guiding the training of a deep learning-based registration method with MI estimation between an image-pair in an end-to-end trainable network.
Pairwise Relation Learning for Semi-supervised Gland Segmentation
In this paper, we propose the pairwise relation-based semi-supervised (PRS^2) model for gland segmentation on histology images.
Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection
In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into an one-class classification-based anomaly detection problem, and thus propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module.
End-to-End Diagnosis and Segmentation Learning from Cardiac Magnetic Resonance Imaging
In this paper, we propose a learning method to train diagnosis models, where our approach is designed to work with relatively small datasets.
