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Found 29 papers, 16 papers with code
Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge
The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients.
Predict and Interpret Health Risk using EHR through Typical Patients
Predicting health risks from electronic health records (EHR) is a topic of recent interest.
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.
Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation
In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution.
Mortality Prediction with Adaptive Feature Importance Recalibration for Peritoneal Dialysis Patients: a deep-learning-based study on a real-world longitudinal follow-up dataset
Here, our objective is to develop a deep learning model for a real-time, individualized, and interpretable mortality prediction model - AICare.
A Comprehensive Benchmark for COVID-19 Predictive Modeling Using Electronic Health Records in Intensive Care
Many deep learning models have been proposed to conduct clinical predictive tasks such as mortality prediction for COVID-19 patients in intensive care units using Electronic Health Record (EHR) data.
MMMNA-Net for Overall Survival Time Prediction of Brain Tumor Patients
Several deep learning-based methods are proposed for the OS time prediction on multi-modal MRI problems.
Transformer Lesion Tracker
Previous methods typically lack the integration of local and global information.
RPLHR-CT Dataset and Transformer Baseline for Volumetric Super-Resolution from CT Scans
In clinical practice, anisotropic volumetric medical images with low through-plane resolution are commonly used due to short acquisition time and lower storage cost.
MedFACT: Modeling Medical Feature Correlations in Patient Health Representation Learning via Feature Clustering
Thus, to address the issues, we expect to group up strongly correlated features and learn feature correlations in a group-wise manner to reduce the learning complexity without losing generality.
eProduct: A Million-Scale Visual Search Benchmark to Address Product Recognition Challenges
This motivated creation of eProduct, a dataset consisting of 2. 5 million product images towards accelerating development in the areas of self-supervised learning, weakly-supervised learning, and multimodal learning, for fine-grained recognition.
De Re Updates
In this paper, we propose a lightweight yet powerful dynamic epistemic logic that captures not only the distinction between de dicto and de re knowledge but also the distinction between de dicto and de re updates.
Deep Transform and Metric Learning Networks
Based on its great successes in inference and denosing tasks, Dictionary Learning (DL) and its related sparse optimization formulations have garnered a lot of research interest.
CovidCare: Transferring Knowledge from Existing EMR to Emerging Epidemic for Interpretable Prognosis
Due to the characteristics of COVID-19, the epidemic develops rapidly and overwhelms health service systems worldwide.
Deep Transform and Metric Learning Network: Wedding Deep Dictionary Learning and Neural Networks
On account of its many successes in inference tasks and denoising applications, Dictionary Learning (DL) and its related sparse optimization problems have garnered a lot of research interest.
StageNet: Stage-Aware Neural Networks for Health Risk Prediction
Compared to the best baseline model, StageNet achieves up to 12% higher AUPRC for risk prediction task on two real-world patient datasets.
ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context
Predicting the patient's clinical outcome from the historical electronic medical records (EMR) is a fundamental research problem in medical informatics.
AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration
It also models the correlation between clinical features to enhance the ones which strongly indicate the health status and thus can maintain a state-of-the-art performance in terms of prediction accuracy while providing qualitative interpretability.
Joint Concept Matching based Learning for Zero-Shot Recognition
Zero-shot learning (ZSL) which aims to recognize unseen object classes by only training on seen object classes, has increasingly been of great interest in Machine Learning, and has registered with some successes.
MUSEFood: Multi-sensor-based Food Volume Estimation on Smartphones
Furthermore, MUSEFood uses the microphone and the speaker to accurately measure the vertical distance from the camera to the food in a noisy environment, thus scaling the size of food in the image to its actual size.
Analysis Dictionary Learning: An Efficient and Discriminative Solution
Discriminative Dictionary Learning (DL) methods have been widely advocated for image classification problems.
Analysis Dictionary Learning based Classification: Structure for Robustness
A consensus structured analysis dictionary and a global classifier are jointly learned in the distributed approach to safeguard the discriminative power and the efficiency of classification.
Structured Analysis Dictionary Learning for Image Classification
We propose a computationally efficient and high-performance classification algorithm by incorporating class structural information in analysis dictionary learning.
Self-Supervised Monocular Image Depth Learning and Confidence Estimation
Convolutional Neural Networks (CNNs) need large amounts of data with ground truth annotation, which is a challenging problem that has limited the development and fast deployment of CNNs for many computer vision tasks.
Context-Aware Mixed Reality: A Framework for Ubiquitous Interaction
Mixed Reality (MR) is a powerful interactive technology that yields new types of user experience.
Semantic Augmented Reality Environment with Material-Aware Physical Interactions
In Augmented Reality (AR) environment, realistic interactions between the virtual and real objects play a crucial role in user experience.
Real-time Geometry-Aware Augmented Reality in Minimally Invasive Surgery
The potential of Augmented Reality (AR) technology to assist minimally invasive surgeries (MIS) lies in its computational performance and accuracy in dealing with challenging MIS scenes.
Recent Developments and Future Challenges in Medical Mixed Reality
Mixed Reality (MR) is of increasing interest within technology-driven modern medicine but is not yet used in everyday practice.
Augmented Reality for Depth Cues in Monocular Minimally Invasive Surgery
In vivo laparoscopic videos used in the tests have demonstrated the robustness and accuracy of our proposed framework on both camera tracking and surface reconstruction, illustrating the potential of our algorithm for depth augmentation and depth-corrected augmented reality in MIS with monocular endoscopes.
Simultaneous Localization and Mapping
Surface Reconstruction
