41 citations · 103 across the 12 of their papers we have counts for
12 papers
Deep Learning and Computer Vision for Glaucoma Detection: A Review
Mona Ashtari-Majlan, Mohammad Mahdi Dehshibi, David Masip
Glaucoma is the leading cause of irreversible blindness worldwide and poses significant diagnostic challenges due to its reliance on subjective evaluation. However, recent advances…
Explanation Shift: How Did the Distribution Shift Impact the Model?
Carlos Mougan, Klaus Broelemann, David Masip +3
As input data distributions evolve, the predictive performance of machine learning models tends to deteriorate. In practice, new input data tend to come without target labels. Then…
ADVISE: ADaptive Feature Relevance and VISual Explanations for Convolutional Neural Networks
Mohammad Mahdi Dehshibi, Mona Ashtari-Majlan, Gereziher Adhane +1
To equip Convolutional Neural Networks (CNNs) with explainability, it is essential to interpret how opaque models take specific decisions, understand what causes the errors, improv…
Explainable, automated urban interventions to improve pedestrian and vehicle safety
Cristina Bustos, Daniel Rhoads, Albert Sole-Ribalta +4
At the moment, urban mobility research and governmental initiatives are mostly focused on motor-related issues, e.g. the problems of congestion and pollution. And yet, we can not d…
A deep convolutional neural network for classification of Aedes albopictus mosquitoes
Gereziher Adhane, Mohammad Mahdi Dehshibi, David Masip
Monitoring the spread of disease-carrying mosquitoes is a first and necessary step to control severe diseases such as dengue, chikungunya, Zika or yellow fever. Previous citizen sc…
On the use of uncertainty in classifying Aedes Albopictus mosquitoes
Gereziher Adhane, Mohammad Mahdi Dehshibi, David Masip
The re-emergence of mosquito-borne diseases (MBDs), which kill hundreds of thousands of people each year, has been attributed to increased human population, migration, and environm…