Research

AbstractThe diagnosis of oral squamous cell carcinoma or oral leukoplakia and the presence or absence of oral epithelial dysplasia is carried by pathologists. In recent years, deep learning has been presented to deal with the automated detection of various pathologies using digital images. One of the main limitations to applying deep learning to histopathological images is the lack of public datasets. In order to fill this gap, a joint effort has been made and a new dataset of histopathological images of oral cancer, named P-NDB-UFES, has been collected, annotated, and analyzed by oral pathologists generating the gold-standard for classification. This dataset is composed of 3763 images of patches of histopathological images with oral squamous cell carcinoma (29%), dysplasia (51.29%), and without dysplasia (18.79%). Next, convolutional neural network (CNN), transformers neural networks, and few-shot learning approaches (i.e., Siamese, Triplet, and ProtoNet) were investigated to classify oral squamous cell carcinoma and the presence or absence of oral dysplasia. Experimental results indicate that the CNNs and transformers models, in general, have no statistically significant difference, with only DenseNet-121 outperforming transformers at a balanced accuracy (BCC) of 91.91%, recall, and precision of 91.93%. Few-shot learning methods were inferior when compared to other methods, with different configurations having statistical differences among themselves. For ProtoNet architectures, the usage of hyperbolic space showed to have a similar behavior to Euclidean distance, however, these results were heavily influenced by the optimizer used.

Abstracttranslated from PortugueseThis article shares the trajectory of the Introcomp university extension project over its nearly 10 years and presents the strategy adopted for the first time entirely online in the 2020 edition. We reflect on our experience applying different approaches to facilitate programming learning for our target audience over the years — transitioning from C to Python, from traditional lectures to fully interactive classes. In doing so, we demonstrate how lessons learned have helped us rethink the use of methodologies, technologies, and digital platforms in teaching programming to high school students during the pandemic. This work advances the state of the art and has the potential to expand the community's understanding of computing education interventions aimed primarily at young audiences.