Comparing vision transformers and convolutional neural networks for image classification: A literature review
Transformers are models that implement a mechanism of self-attention, individually
weighting the importance of each part of the input data. Their use in image classification …
weighting the importance of each part of the input data. Their use in image classification …
Interpreting deep machine learning models: an easy guide for oncologists
Healthcare agents, in particular in the oncology field, are currently collecting vast amounts of
diverse patient data. In this context, some decision-support systems, mostly based on deep …
diverse patient data. In this context, some decision-support systems, mostly based on deep …
[HTML][HTML] Neural network for ordinal classification of imbalanced data by minimizing a Bayesian cost
M Lázaro, AR Figueiras-Vidal - Pattern Recognition, 2023 - Elsevier
Ordinal classification of imbalanced data is a challenging problem that appears in many real
world applications. The challenge is to simultaneously consider the order of the classes and …
world applications. The challenge is to simultaneously consider the order of the classes and …
Deep and interpretable regression models for ordinal outcomes
Outcomes with a natural order commonly occur in prediction problems and often the
available input data are a mixture of complex data like images and tabular predictors. Deep …
available input data are a mixture of complex data like images and tabular predictors. Deep …
The impact of heterogeneous distance functions on missing data imputation and classification performance
This work performs an in-depth study of the impact of distance functions on K-Nearest
Neighbours imputation of heterogeneous datasets. Missing data is generated at several …
Neighbours imputation of heterogeneous datasets. Missing data is generated at several …
Missing data imputation via denoising autoencoders: the untold story
Missing data consists in the lack of information in a dataset and since it directly influences
classification performance, neglecting it is not a valid option. Over the years, several studies …
classification performance, neglecting it is not a valid option. Over the years, several studies …
Deriving disease modules from the compressed transcriptional space embedded in a deep autoencoder
Disease modules in molecular interaction maps have been useful for characterizing
diseases. Yet biological networks, that commonly define such modules are incomplete and …
diseases. Yet biological networks, that commonly define such modules are incomplete and …
Bioinformatics and medicine in the era of deep learning
Many of the current scientific advances in the life sciences have their origin in the intensive
use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by …
use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by …
Distinguishing between Crohn's disease and ulcerative colitis using deep learning models with interpretability
J Maurício, I Domingues - Pattern Analysis and Applications, 2024 - Springer
Crohn's disease and ulcerative colitis are two chronic diseases that cause inflammation in
the tissues of the entire gastrointestinal tract and are described by the term inflammatory …
the tissues of the entire gastrointestinal tract and are described by the term inflammatory …
Knowledge distillation of vision transformers and convolutional networks to predict inflammatory bowel disease
J Maurício, I Domingues - Iberoamerican Congress on Pattern Recognition, 2023 - Springer
Inflammatory bowel disease is a chronic disease of unknown cause that can affect the entire
gastrointestinal tract, from the mouth to the anus. It is important for patients with this …
gastrointestinal tract, from the mouth to the anus. It is important for patients with this …