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Found 8 papers, 1 papers with code
Uncertainty-guided Contrastive Learning for Single Source Domain Generalisation
In the context of single domain generalisation, the objective is for models that have been exclusively trained on data from a single domain to demonstrate strong performance when confronted with various unfamiliar domains.
COVID-19 Computer-aided Diagnosis through AI-assisted CT Imaging Analysis: Deploying a Medical AI System
Computer-aided diagnosis (CAD) systems stand out as potent aids for physicians in identifying the novel Coronavirus Disease 2019 (COVID-19) through medical imaging modalities.
Domain adaptation, Explainability & Fairness in AI for Medical Image Analysis: Diagnosis of COVID-19 based on 3-D Chest CT-scans
The paper presents the DEF-AI-MIA COV19D Competition, which is organized in the framework of the 'Domain adaptation, Explainability, Fairness in AI for Medical Image Analysis (DEF-AI-MIA)' Workshop of the 2024 Computer Vision and Pattern Recognition (CVPR) Conference.
FaceRNET: a Facial Expression Intensity Estimation Network
This paper presents our approach for Facial Expression Intensity Estimation from videos.
A Deep Neural Architecture for Harmonizing 3-D Input Data Analysis and Decision Making in Medical Imaging
Harmonizing the analysis of data, especially of 3-D image volumes, consisting of different number of slices and annotated per volume, is a significant problem in training and using deep neural networks in various applications, including medical imaging.
AI-MIA: COVID-19 Detection & Severity Analysis through Medical Imaging
This paper presents the baseline approach for the organized 2nd Covid-19 Competition, occurring in the framework of the AIMIA Workshop in the European Conference on Computer Vision (ECCV 2022).
MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis
In this paper we present the COV19-CT-DB database which is annotated for COVID-19, consisting of about 5, 000 3-D CT scans, We have split the database in training, validation and test datasets.
NTUAAILS at SemEval-2020 Task 11: Propaganda Detection and Classification with biLSTMs and ELMo
This paper describes the NTUAAILS submission for SemEval 2020 Task 11 Detection of Propaganda Techniques in News Articles.
