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Found 9 papers, 5 papers with code
Fundus2Video: Cross-Modal Angiography Video Generation from Static Fundus Photography with Clinical Knowledge Guidance
In this work, we pioneer dynamic FFA video generation from static CF images.
UWF-RI2FA: Generating Multi-frame Ultrawide-field Fluorescein Angiography from Ultrawide-field Retinal Imaging Improves Diabetic Retinopathy Stratification
The DeepDRiD dataset was used to externally assess the contribution of generated UWF-FA images to DR classification, using area under the receiver operating characteristic curve (AUROC) as outcome metrics.
Choroidal Vessel Segmentation on Indocyanine Green Angiography Images via Human-in-the-Loop Labeling
Thus, the study aims to develop a high-precision choroidal vessel segmentation model with limited labor using HITL framework.
Object Detectors in the Open Environment: Challenges, Solutions, and Outlook
This paper aims to bridge this gap by conducting a comprehensive review and analysis of object detectors in open environments.
Less is More: Fewer Interpretable Region via Submodular Subset Selection
For incorrectly predicted samples, our method achieves gains of 81. 0% and 18. 4% compared to the HSIC-Attribution algorithm in the average highest confidence and Insertion score respectively.
Ranked #1 on
Error Understanding
on CUB-200-2011
mCL-NER: Cross-Lingual Named Entity Recognition via Multi-view Contrastive Learning
A multi-view contrastive learning framework is introduced to encompass semantic contrasts between source, codeswitched, and target sentences, as well as contrasts among token-to-token relations.
Sim2Word: Explaining Similarity with Representative Attribute Words via Counterfactual Explanations
Different from existing models, in this paper, we propose a new interpretation method that explains the image similarity models by salience maps and attribute words.
Online estimating weight of white Pekin duck carcass by computer vision
The model estimated the weight of duck carcasses precisely with a mean abstract deviation (MAD) of 58. 8 grams and a mean relative error (MRE) of 2. 15% in the testing dataset.
Understanding Heart-Failure Patients EHR Clinical Features via SHAP Interpretation of Tree-Based Machine Learning Model Predictions
We examined whether machine learning models, more specifically the XGBoost model, can accurately predict patient stage based on EHR, and we further applied the SHapley Additive exPlanations (SHAP) framework to identify informative features and their interpretations.
