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Found 16 papers, 12 papers with code
CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation
Also, we propose a scale attention module implicitly emphasizing the most salient feature maps among multiple scales so that the CNN is adaptive to the size of an object.
Validation of non-negative matrix factorization for assessment of atomic pair-distribution function (PDF) data in a real-time streaming context
We validate the use of matrix factorization for the automatic identification of relevant components from atomic pair distribution function (PDF) data.
The smallest number of vertices in a 2-arc-strong digraph which has no good pair
They also proved that every digraph on at most 6 vertices and arc-connectivity at least 2 has a good pair and gave an example of a 2-arc-strong digraph $D$ on 10 vertices with independence number 4 that has no good pair.
Combinatorics
Contrastive Learning of Relative Position Regression for One-Shot Object Localization in 3D Medical Images
To address this problem, we present a one-shot framework for organ and landmark localization in volumetric medical images, which does not need any annotation during the training stage and could be employed to locate any landmarks or organs in test images given a support (reference) image during the inference stage.
A fast two-stage algorithm for non-negative matrix factorization in streaming data
In the second stage, an interior point method is adopted to accelerate the local convergence.
Optimization and Control Numerical Analysis Numerical Analysis 65K10, 90C26 G.1.6; F.2.1
Automatic Segmentation of Gross Target Volume of Nasopharynx Cancer using Ensemble of Multiscale Deep Neural Networks with Spatial Attention
Delineation of Gross Target Volume (GTV) from medical images such as CT and MRI images is a prerequisite for radiotherapy.
Automatic Segmentation of Organs-at-Risk from Head-and-Neck CT using Separable Convolutional Neural Network with Hard-Region-Weighted Loss
Despite the stateof-the-art performance achieved by Convolutional Neural Networks (CNNs) for automatic segmentation of OARs, existing methods do not provide uncertainty estimation of the segmentation results for treatment planning, and their accuracy is still limited by several factors, including the low contrast of soft tissues in CT, highly imbalanced sizes of OARs and large inter-slice spacing.
SS-CADA: A Semi-Supervised Cross-Anatomy Domain Adaptation for Coronary Artery Segmentation
To solve this problem, we propose a Semi-Supervised Cross-Anatomy Domain Adaptation (SS-CADA) which requires only limited annotations for coronary arteries in XAs.
Domain Composition and Attention for Unseen-Domain Generalizable Medical Image Segmentation
First, we present a domain composition method that represents one certain domain by a linear combination of a set of basis representations (i. e., a representation bank).
One-shot Weakly-Supervised Segmentation in Medical Images
Deep neural networks usually require accurate and a large number of annotations to achieve outstanding performance in medical image segmentation.
Contrastive Domain Disentanglement for Generalizable Medical Image Segmentation
To tackle this deficiency, we propose Contrastive Domain Disentangle (CDD) network for generalizable medical image segmentation.
Learning towards Synchronous Network Memorizability and Generalizability for Continual Segmentation across Multiple Sites
In clinical practice, a segmentation network is often required to continually learn on a sequential data stream from multiple sites rather than a consolidated set, due to the storage cost and privacy restriction.
Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures
To solve this problem, we propose Contrastive Semi-supervised learning for Cross Anatomy Domain Adaptation (CS-CADA) that adapts a model to segment similar structures in a target domain, which requires only limited annotations in the target domain by leveraging a set of existing annotated images of similar structures in a source domain.
PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation
Existing toolkits mainly focus on fully supervised segmentation and require full and accurate pixel-level annotations that are time-consuming and difficult to acquire for segmentation tasks, which makes learning from imperfect labels highly desired for reducing the annotation cost.
CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image Segmentation
First, a disentangle network is proposed to decompose an image into a domain-invariant anatomical representation and a domain-specific style code, where the former is sent to a segmentation model that is not affected by the domain shift, and the disentangle network is regularized by a decoder that combines the anatomical and style codes to reconstruct the input image.
UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation
The different predictions in these duplicated heads are used to obtain pseudo labels for unlabeled target-domain images and their uncertainty to identify reliable pseudo labels.
