Histopathological analysis is an essential exam for detecting various types of cancer. The process is traditionally time-consuming and laborious. Taking advantage of deep learning models, assisting the pathologist in the diagnosis process is possible. In this work, a study was carried out based on the DenseNet neural network. It consisted of changing its architecture through combinations of Transformer and MBConv blocks to investigate its impact on classifying histopathological images of penile cancer. Due to the limited number of samples in this dataset, pre-training is performed on another larger lung and colon cancer histopathological image dataset. Various combinations of these architectural components were systematically evaluated to compare their performance. The results indicate significant improvements in feature representation, demonstrating the effectiveness of these combined elements resulting in an F1-Score of up to 95.78%. Its diagnostic performance confirms the importance of deep learning techniques in men’s health.

Penile cancer has an incidence strongly linked to sociocultural factors, being more common in underdeveloped countries like Brazil, where it represents approximately 2% of cancers affecting men. This dataset was created to address the scarcity of publicly available resources for classifying histopathological images in penile cancer research. The images were collected in 2021 from tissue samples obtained through biopsies of patients undergoing treatment for penile cancer. After staining with Hematoxylin and Eosin (H&E), the tissue samples were photographed using a Leica ICC50 HD camera attached to a bright-field microscope (Leica DM500). The dataset comprises 194 high-resolution images (2048 × 1536 pixels), categorized by magnification (40X and 100X) and pathological classification (Tumor or Non-Tumor). Metadata includes additional information such as histological grade and, for some images, HPV status. Although previous works have focused primarily on binary classification tasks, the dataset includes additional labels, such as histological grade and HPV (Human Papilloma Virus) presence, which provide opportunities for multi-label classification or other types of predictive modelling. These extended labels enhance the dataset’s versatility for more complex tasks in medical image analysis. The dataset holds significant reuse potential for machine learning tasks beyond binary classification, allowing researchers to explore additional layers of analysis, such as HPV detection and histological grading. It can also be used for model benchmarking and comparative studies in cancer research, contributing to developing new diagnostic tools. The dataset and metadata are available for further research and model development.

Este artigo analisa as políticas públicas de inclusão digital nas universidades federais das capitais do nordeste brasileiro entre 2020 e 2023. O objetivo desse estudo é investigar as ações adotadas pelas universidades para promover a inclusão digital. Abordam-se as iniciativas implementadas para modernizar a infraestrutura tecnológica, ampliar o acesso à internet e capacitar a comunidade acadêmica para o uso eficaz das Tecnologias da Informação e Comunicação (TICs). Por meio de uma abordagem qualitativa, que inclui pesquisa bibliográfica e análise documental, o estudo revela avanços significativos na redução da exclusão digital e na melhoria da experiência acadêmica. No entanto, também são identificados desafios persistentes, como adaptação a nova modalidade de ensino e a dificuldades no manuseio de novas tecnologias. A conclusão destaca a importância de um compromisso contínuo e colaborativo para garantir a sustentabilidade e a eficácia das políticas de inclusão digital, promovendo uma educação mais equitativa e inclusiva na região.