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Found 11 papers, 3 papers with code
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.
Parallel feature selection based on the trace ratio criterion
The experiments show that our method can produce a small set of features in a fraction of the amount of time by the other methods under comparison.
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.
Common Limitations of Image Processing Metrics: A Picture Story
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.
Long Short Term Memory Networks for Bandwidth Forecasting in Mobile Broadband Networks under Mobility
Bandwidth forecasting in Mobile Broadband (MBB) networks is a challenging task, particularly when coupled with a degree of mobility.
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.
Stacked dense optical flows and dropout layers to predict sperm motility and morphology
To solve this regression task of predicting motility and morphology, stacked dense optical flows and extracted original frames from sperm videos were used with the modified state of the art convolution neural networks.
Extracting temporal features into a spatial domain using autoencoders for sperm video analysis
In this paper, we present a two-step deep learning method that is used to predict sperm motility and morphology-based on video recordings of human spermatozoa.
Multi-Modal Machine Learning for Flood Detection in News, Social Media and Satellite Sequences
In this paper we present our methods for the MediaEval 2019 Mul-timedia Satellite Task, which is aiming to extract complementaryinformation associated with adverse events from Social Media andsatellites.
The Medico-Task 2018: Disease Detection in the Gastrointestinal Tract using Global Features and Deep Learning
In this paper, we present our approach for the 2018 Medico Task classifying diseases in the gastrointestinal tract.
Top-Down Saliency Detection Driven by Visual Classification
This paper presents an approach for top-down saliency detection guided by visual classification tasks.
