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Found 8 papers, 1 papers with code
Speech emotion recognition from voice messages recorded in the wild
The pre-trained Unispeech-L model and its combination with eGeMAPS achieved the highest results, with 61. 64% and 55. 57% Unweighted Accuracy (UA) for 3-class valence and arousal prediction respectively, a 10% improvement over baseline models.
Emotional Voice Messages (EMOVOME) database: emotion recognition in spontaneous voice messages
For speech, we used the standard eGeMAPS feature set and support vector machines, obtaining 49. 27% and 44. 71% unweighted accuracy for valence and arousal respectively.
Attention to detail: inter-resolution knowledge distillation
The development of computer vision solutions for gigapixel images in digital pathology is hampered by significant computational limitations due to the large size of whole slide images.
A Spitzoid Tumor dataset with clinical metadata and Whole Slide Images for Deep Learning models
Spitzoid tumors (ST) are a group of melanocytic tumors of high diagnostic complexity.
HistoColAi: An Open-Source Web Platform for Collaborative Digital Histology Image Annotation with AI-Driven Predictive Integration
Digital pathology has become a standard in the pathology workflow due to its many benefits.
Circumpapillary OCT-Focused Hybrid Learning for Glaucoma Grading Using Tailored Prototypical Neural Networks
Glaucoma is one of the leading causes of blindness worldwide and Optical Coherence Tomography (OCT) is the quintessential imaging technique for its detection.
An Attention-based Weakly Supervised framework for Spitzoid Melanocytic Lesion Diagnosis in WSI
Nevertheless, no automatic CAD systems have yet been proposed for the analysis of spitzoid lesions.
Glaucoma Detection From Raw Circumapillary OCT Images Using Fully Convolutional Neural Networks
Nowadays, glaucoma is the leading cause of blindness worldwide.
