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Found 11 papers, 3 papers with code
Infant hip screening using multi-class ultrasound scan segmentation
Developmental dysplasia of the hip (DDH) is a condition in infants where the femoral head is incorrectly located in the hip joint.
Triple-View Feature Learning for Medical Image Segmentation
The confidence of each model gets improved through the other two views of the feature learning.
Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images
In the second stage, the decentralized partially labeled data are exploited to learn an energy-based multi-label classifier for the common classes.
Revisiting Vicinal Risk Minimization for Partially Supervised Multi-Label Classification Under Data Scarcity
Due to the high human cost of annotation, it is non-trivial to curate a large-scale medical dataset that is fully labeled for all classes of interest.
Contour-Hugging Heatmaps for Landmark Detection
We find that this method not only achieves localisation results on par with other state-of-the-art methods but also an uncertainty score which correlates with the true error for each landmark thereby bringing an overall step change in what a generic computer vision method for landmark detection should be capable of.
Federated Contrastive Learning for Decentralized Unlabeled Medical Images
A label-efficient paradigm in computer vision is based on self-supervised contrastive pre-training on unlabeled data followed by fine-tuning with a small number of labels.
Negational Symmetry of Quantum Neural Networks for Binary Pattern Classification
In this work, we provide some theoretical insight into the properties of QNNs by presenting and analyzing a new form of invariance embedded in QNNs for both quantum binary classification and quantum representation learning, which we term negational symmetry.
Quadruple Augmented Pyramid Network for Multi-class COVID-19 Segmentation via CT
COVID-19, a new strain of coronavirus disease, has been one of the most serious and infectious disease in the world.
Towards Robust Partially Supervised Multi-Structure Medical Image Segmentation on Small-Scale Data
To bridge the methodological gaps in partially supervised learning (PSL) under data scarcity, we propose Vicinal Labels Under Uncertainty (VLUU), a simple yet efficient framework utilizing the human structure similarity for partially supervised medical image segmentation.
Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy
The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies.
RAR-U-Net: a Residual Encoder to Attention Decoder by Residual Connections Framework for Spine Segmentation under Noisy Labels
Segmentation algorithms for medical images are widely studied for various clinical and research purposes.
