21 citations · 36 across the 7 of their papers we have counts for
7 papers
Deep Fast Machine Learning Utils: A Python Library for Streamlined Machine Learning Prototyping
Fabi Prezja
Machine learning (ML) research and application often involve time-consuming steps such as model architecture prototyping, feature selection, and dataset preparation. To support the…
Machine Learning Predicts Upper Secondary Education Dropout as Early as the End of Primary School
Maria Psyridou, Fabi Prezja, Minna Torppa +3
Education plays a pivotal role in alleviating poverty, driving economic growth, and empowering individuals, thereby significantly influencing societal and personal development. How…
Deep Fast Vision: A Python Library for Accelerated Deep Transfer Learning Vision Prototyping
Fabi Prezja
Deep learning-based vision is characterized by intricate frameworks that often necessitate a profound understanding, presenting a barrier to newcomers and limiting broad adoption.…
Exploring the Efficacy of Base Data Augmentation Methods in Deep Learning-Based Radiograph Classification of Knee Joint Osteoarthritis
Fabi Prezja, Leevi Annala, Sampsa Kiiskinen +1
Diagnosing knee joint osteoarthritis (KOA), a major cause of disability worldwide, is challenging due to subtle radiographic indicators and the varied progression of the disease. U…
Adaptive Variance Thresholding: A Novel Approach to Improve Existing Deep Transfer Vision Models and Advance Automatic Knee-Joint Osteoarthritis Classification
Fabi Prezja, Leevi Annala, Sampsa Kiiskinen +2
Knee-Joint Osteoarthritis (KOA) is a prevalent cause of global disability and is inherently complex to diagnose due to its subtle radiographic markers and individualized progressio…
Synthesizing Bidirectional Temporal States of Knee Osteoarthritis Radiographs with Cycle-Consistent Generative Adversarial Neural Networks
Fabi Prezja, Leevi Annala, Sampsa Kiiskinen +2
Knee Osteoarthritis (KOA), a leading cause of disability worldwide, is challenging to detect early due to subtle radiographic indicators. Diverse, extensive datasets are needed but…
Improving Performance in Colorectal Cancer Histology Decomposition using Deep and Ensemble Machine Learning
Fabi Prezja, Leevi Annala, Sampsa Kiiskinen +4
In routine colorectal cancer management, histologic samples stained with hematoxylin and eosin are commonly used. Nonetheless, their potential for defining objective biomarkers for…