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Found 14 papers, 8 papers with code
Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directions
To answer these questions, a comprehensive and deep survey of the challenges, opportunities, and future directions of HFMs is presented in this survey.
Dynamic Snake Convolution based on Topological Geometric Constraints for Tubular Structure Segmentation
In this work, we note the specificity of tubular structures and use this knowledge to guide our DSCNet to simultaneously enhance perception in three stages: feature extraction, feature fusion, and loss constraint.
Knowledge Boosting: Rethinking Medical Contrastive Vision-Language Pre-Training
To address these issues, we propose the Knowledge-Boosting Contrastive Vision-Language Pre-training framework (KoBo), which integrates clinical knowledge into the learning of vision-language semantic consistency.
JCCS-PFGM: A Novel Circle-Supervision based Poisson Flow Generative Model for Multiphase CECT Progressive Low-Dose Reconstruction with Joint Condition
Multiphase contrast-enhanced computed tomography (CECT) scan is clinically significant to demonstrate the anatomy at different phases.
Segment Anything in Medical Images
Medical image segmentation is a critical component in clinical practice, facilitating accurate diagnosis, treatment planning, and disease monitoring.
Geometric Visual Similarity Learning in 3D Medical Image Self-supervised Pre-training
We propose a novel visual similarity learning paradigm, Geometric Visual Similarity Learning, which embeds the prior of topological invariance into the measurement of the inter-image similarity for consistent representation of semantic regions.
Yield Evaluation of Citrus Fruits based on the YoloV5 compressed by Knowledge Distillation
In the field of planting fruit trees, pre-harvest estimation of fruit yield is important for fruit storage and price evaluation.
Contrastive Re-localization and History Distillation in Federated CMR Segmentation
In this work, a cross-center cross-sequence medical image segmentation FL framework (FedCRLD) is proposed for the first time to facilitate multi-center multi-sequence CMR segmentation.
FFCNet: Fourier Transform-Based Frequency Learning and Complex Convolutional Network for Colon Disease Classification
Reliable automatic classification of colonoscopy images is of great significance in assessing the stage of colonic lesions and formulating appropriate treatment plans.
XMorpher: Full Transformer for Deformable Medical Image Registration via Cross Attention
An effective backbone network is important to deep learning-based Deformable Medical Image Registration (DMIR), because it extracts and matches the features between two images to discover the mutual correspondence for fine registration.
MNet: Rethinking 2D/3D Networks for Anisotropic Medical Image Segmentation
The nature of thick-slice scanning causes severe inter-slice discontinuities of 3D medical images, and the vanilla 2D/3D convolutional neural networks (CNNs) fail to represent sparse inter-slice information and dense intra-slice information in a balanced way, leading to severe underfitting to inter-slice features (for vanilla 2D CNNs) and overfitting to noise from long-range slices (for vanilla 3D CNNs).
CPNet: Cycle Prototype Network for Weakly-supervised 3D Renal Compartments Segmentation on CT Images
We propose a novel weakly supervised learning framework, Cycle Prototype Network, for 3D renal compartment segmentation.
EnMcGAN: Adversarial Ensemble Learning for 3D Complete Renal Structures Segmentation
3)We propose the adversarial weighted ensemble module which uses the trained discriminators to evaluate the quality of segmented structures, and normalizes these evaluation scores for the ensemble weights directed at the input image, thus enhancing the ensemble results.
Deep Complementary Joint Model for Complex Scene Registration and Few-shot Segmentation on Medical Images
Deep learning-based medical image registration and segmentation joint models utilize the complementarity (augmentation data or weakly supervised data from registration, region constraints from segmentation) to bring mutual improvement in complex scene and few-shot situation.
