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Found 7 papers, 0 papers with code
Integrating Feature and Image Pyramid: A Lung Nodule Detector Learned in Curriculum Fashion
Meanwhile, our sampling strategy halves the training time of the proposal network on LUNA16.
Cascaded Generative and Discriminative Learning for Microcalcification Detection in Breast Mammograms
Most of the previous mC detection methods belong to discriminative models, where classifiers are exploited to distinguish mCs from other backgrounds.
Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification
Mammogram benign or malignant classification with only image-level labels is challenging due to the absence of lesion annotations.
Act Like a Radiologist: Towards Reliable Multi-view Correspondence Reasoning for Mammogram Mass Detection
In this paper, we propose an Anatomy-aware Graph convolutional Network (AGN), which is tailored for mammogram mass detection and endows existing detection methods with multi-view reasoning ability.
Context-LGM: Leveraging Object-Context Relation for Context-Aware Object Recognition
Finally, to implement this contextual posterior, we introduce a Transformer that takes the object's information as a reference and locates correlated contextual factors.
Domain Invariant Model with Graph Convolutional Network for Mammogram Classification
To resolve this problem, we propose a novel framework, namely Domain Invariant Model with Graph Convolutional Network (DIM-GCN), which only exploits invariant disease-related features from multiple domains.
Graph Convolution Based Cross-Network Multi-Scale Feature Fusion for Deep Vessel Segmentation
To tackle the challenges caused by the sparsity and anisotropy of vessels, a higher percentage of graph nodes are distributed in areas that potentially contain vessels while a higher percentage of edges follow the orientation of potential nearbyvessels.
