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Found 8 papers, 2 papers with code
Integrating Contrastive Learning into a Multitask Transformer Model for Effective Domain Adaptation
While speech emotion recognition (SER) research has made significant progress, achieving generalization across various corpora continues to pose a problem.
Self-supervised learning for hotspot detection and isolation from thermal images
Our experiments also show self-supervised learning as a strong contender of supervised learning, providing competitive metrics for hotspot detection, with the highest accuracy of our approach being 97%.
Multi-modal Graph Neural Network for Early Diagnosis of Alzheimer's Disease from sMRI and PET Scans
In this study, we demonstrate how brain networks can be created from sMRI or PET images and be used in a population graph framework that can combine phenotypic information with imaging features of these brain networks.
Marshall-Olkin Power-Law Distributions in Length-Frequency of Entities
Entities have different forms in different linguistic tasks and researchers treat those different forms as different concepts.
Named Entity Analysis and Extraction with Uncommon Words
Most previous research treats named entity extraction and classification as an end-to-end task.
Deep neural network ensemble by data augmentation and bagging for skin lesion classification
This work summarizes our submission for the Task 3: Disease Classification of ISIC 2018 challenge in Skin Lesion Analysis Towards Melanoma Detection.
Automatic segmentation of skin lesions using deep learning
This paper summarizes the method used in our submission to Task 1 of the International Skin Imaging Collaboration's (ISIC) Skin Lesion Analysis Towards Melanoma Detection challenge held in 2018.
Segmentation of Clustered Nuclei With Shape Markers and Marking Function
We present a method to separate clustered nuclei from fluorescence microscopy cellular images, using shape markers and marking function in a watershed-like algorithm.
