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Found 8 papers, 4 papers with code
GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection
Integrating real-time artificial intelligence (AI) systems in clinical practices faces challenges such as scalability and acceptance.
Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations
Deep learning has in recent years achieved immense success in all areas of computer vision and has the potential of assisting medical doctors in analyzing visual content for disease and other abnormalities.
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
Polyps are well-known cancer precursors identified by colonoscopy.
Visual Sentiment Analysis: A Natural DisasterUse-case Task at MediaEval 2021
The Visual Sentiment Analysis task is being offered for the first time at MediaEval.
MedAI: Transparency in Medical Image Segmentation
MedAI: Transparency in Medical Image Segmentation is a challenge held for the first time at the Nordic AI Meet that focuses on medical image segmentation and transparency in machine learning (ML)-based systems.
Meta-learning with implicit gradients in a few-shot setting for medical image segmentation
To our knowledge, this is the first work that exploits iMAML for medical image segmentation and explores the strength of the model on scenarios such as meta-training on unique and mixed instances of lesion datasets.
Pyramid-Focus-Augmentation: Medical Image Segmentation with Step-Wise Focus
Segmentation of findings in the gastrointestinal tract is a challenging but also an important task which is an important building stone for sufficient automatic decision support systems.
Visual Sentiment Analysis from Disaster Images in Social Media
While sentiment analysis of text streams has been widely explored in literature, sentiment analysis from images and videos is relatively new.
