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Found 14 papers, 5 papers with code
GenAI for Simulation Model in Model-Based Systems Engineering
Generative AI (GenAI) has demonstrated remarkable capabilities in code generation, and its integration into complex product modeling and simulation code generation can significantly enhance the efficiency of the system design phase in Model-Based Systems Engineering (MBSE).
Hierarchical LoG Bayesian Neural Network for Enhanced Aorta Segmentation
Accurate segmentation of the aorta and its associated arch branches is crucial for diagnosing aortic diseases.
Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network
To address these challenges, in this work, we propose a new SSF, called \proposed, {for segmenting any anatomical structures in 3D medical images using only a single annotated slice per training and testing volume.}
FCNR: Fast Compressive Neural Representation of Visualization Images
We present FCNR, a fast compressive neural representation for tens of thousands of visualization images under varying viewpoints and timesteps.
Boosting Medical Image Classification with Segmentation Foundation Model
The Segment Anything Model (SAM) exhibits impressive capabilities in zero-shot segmentation for natural images.
Self Pre-training with Topology- and Spatiality-aware Masked Autoencoders for 3D Medical Image Segmentation
But, existing MAE pre-training methods, which were specifically developed with the ViT architecture, lack the ability to capture geometric shape and spatial information, which is critical for medical image segmentation tasks.
Path-GPTOmic: A Balanced Multi-modal Learning Framework for Survival Outcome Prediction
For predicting cancer survival outcomes, standard approaches in clinical research are often based on two main modalities: pathology images for observing cell morphology features, and genomic (e. g., bulk RNA-seq) for quantifying gene expressions.
Current and future directions in network biology
Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales.
SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation
Our new method is iterative and consists of two main stages: (1) segmentation model training; (2) expanding the labeled set by using the trained segmentation model, an unlabeled set, SAM, and domain-specific knowledge.
SwIPE: Efficient and Robust Medical Image Segmentation with Implicit Patch Embeddings
Modern medical image segmentation methods primarily use discrete representations in the form of rasterized masks to learn features and generate predictions.
Graph-Free Learning in Graph-Structured Data: A More Efficient and Accurate Spatiotemporal Learning Perspective
In this paper, with extensive and deep-going experiments, we comprehensively analyze existing spatiotemporal graph learning models and reveal that extracting adjacency matrices with carefully design strategies, which are viewed as the key of enhancing performance on graph learning, are largely ineffective.
Keep Your Friends Close & Enemies Farther: Debiasing Contrastive Learning with Spatial Priors in 3D Radiology Images
Understanding of spatial attributes is central to effective 3D radiology image analysis where crop-based learning is the de facto standard.
ConvFormer: Combining CNN and Transformer for Medical Image Segmentation
(2) A residual-shaped hybrid stem based on a combination of convolutions and Enhanced DeTrans is developed to capture both local and global representations to enhance representation ability.
A Point in the Right Direction: Vector Prediction for Spatially-aware Self-supervised Volumetric Representation Learning
High annotation costs and limited labels for dense 3D medical imaging tasks have recently motivated an assortment of 3D self-supervised pretraining methods that improve transfer learning performance.
