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Found 11 papers, 5 papers with code
Leveraging Prototype Patient Representations with Feature-Missing-Aware Calibration to Mitigate EHR Data Sparsity
This module is sensitive to the missing feature status, enabling the model to better judge the reliability of each feature.
M$^3$Fair: Mitigating Bias in Healthcare Data through Multi-Level and Multi-Sensitive-Attribute Reweighting Method
In healthcare AI, these attributes can play a significant role in determining the quality of care that individuals receive.
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.
Deep 3D Vessel Segmentation based on Cross Transformer Network
In CTN, a transformer module is constructed in parallel to a U-Net to learn long-distance dependencies between different anatomical regions; and these dependencies are communicated to the U-Net at multiple stages to endow it with global awareness.
Computer-aided Tuberculosis Diagnosis with Attribute Reasoning Assistance
Although deep learning algorithms have been intensively developed for computer-aided tuberculosis diagnosis (CTD), they mainly depend on carefully annotated datasets, leading to much time and resource consumption.
GREN: Graph-Regularized Embedding Network for Weakly-Supervised Disease Localization in X-ray Images
To model the relationship, we propose the Graph Regularized Embedding Network (GREN), which leverages the intra-image and inter-image information to locate diseases on chest X-ray images.
Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction
The main idea is to use CNNs to learn local appearances of vessels in image crops while using another point-cloud network to learn the global geometry of vessels in the entire image.
Rethinking the Extraction and Interaction of Multi-Scale Features for Vessel Segmentation
Analyzing the morphological attributes of blood vessels plays a critical role in the computer-aided diagnosis of many cardiovascular and ophthalmologic diseases.
Segmentation-based Method combined with Dynamic Programming for Brain Midline Delineation
The midline related pathological image features are crucial for evaluating the severity of brain compression caused by stroke or traumatic brain injury (TBI).
Globally Guided Progressive Fusion Network for 3D Pancreas Segmentation
Recently 3D volumetric organ segmentation attracts much research interest in medical image analysis due to its significance in computer aided diagnosis.
AADS: Augmented Autonomous Driving Simulation using Data-driven Algorithms
Our augmented approach combines the flexibility in a virtual environment (e. g., vehicle movements) with the richness of the real world to allow effective simulation of anywhere on earth.
