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Found 6 papers, 5 papers with code
Domain adaptation based self-correction model for COVID-19 infection segmentation in CT images
DASC-Net consists of a novel attention and feature domain enhanced domain adaptation model (AFD-DA) to solve the domain shifts and a self-correction learning process to refine segmentation results.
Free-form tumor synthesis in computed tomography images via richer generative adversarial network
The network is composed of a new richer convolutional feature enhanced dilated-gated generator (RicherDG) and a hybrid loss function.
MGCT: Mutual-Guided Cross-Modality Transformer for Survival Outcome Prediction using Integrative Histopathology-Genomic Features
The rapidly emerging field of deep learning-based computational pathology has shown promising results in utilizing whole slide images (WSIs) to objectively prognosticate cancer patients.
Inter- and intra-uncertainty based feature aggregation model for semi-supervised histopathology image segmentation
To address these issues, we first propose a novel inter- and intra-uncertainty regularization method to measure and constrain both inter- and intra-inconsistencies in the teacher-student architecture.
Dual-level Semantic Transfer Deep Hashing for Efficient Social Image Retrieval
Specifically, we design a complementary dual-level semantic transfer mechanism to efficiently discover the potential semantics of tags and seamlessly transfer them into binary hash codes.
SigNet: A Novel Deep Learning Framework for Radio Signal Classification
More interestingly, our proposed models behave extremely well in small-sample learning when only a small training dataset is provided.
