Cell nuclei segmentation in histopathological images is essential for diagnosing oral epithelial dysplasia, a condition associated with an increased risk of oral cancer. Deep learning models have demonstrated significant potential in this task, but challenges persist due to variations in staining, tissue morphology, and artifacts. This study investigates segmentation models and proposes ensemble approaches to improve instance segmentation in OED histological images. The ensemble integrates diverse segmentation models using different voting rules, with the$D_{C^{-}}$weighted averaging achieving the best results. The proposed method obtained an accuracy of$\mathbf{9 4. 0 9 \%}$and a Dice coefficient of 0.9461, surpassing individual models and demonstrating significant improvement. Comparative analysis with the literature shows that the ensemble achieved competitive performance across multiple datasets. These results reinforce the potential of ensemble learning to enhance segmentation accuracy, contributing to the development of robust computer-aided diagnosis systems.

Cancer remains a major global health threat due to its high mortality rate and the challenges associated with its treatment, especially in the later stages. It is very important that the patient identify the cancer as soon as possible to increase recovery chances, and for this, histological images are often used. These images are often looked at by a professional, who analyzes it and categorizes the tissue in labels, but it is often a difficult process for these professionals to analyze a great number of images, and that is why it is used computer vision and neural networks to aid in the identification steps of the disease. A very important network structure that can be used in computer vision is a model called U-Net, named after the U shape made by the decoding and encoding blocks, this network model extracts information while changing the size of the image, being able to get the finest to the more general features of the image. This network can also use an attention system to further improve the feature extraction phase and aid in even better segmentation, with multiple attention models for different purposes. Therefore, this study shows how these attention channels can be tweaked to improve the model results, allowing different types of attention to improve in areas of weakness of the model.

Cancer is a major health concern in Brazil and globally and is characterized by its high incidence and mortality rates. Diagnosis typically involves the preparation and microscopic analysis of tissue samples, which are often stained with hematoxylin and eosin (H&E). However, color variation in these images poses a significant challenge for computeraided diagnosis systems. This study explored dictionary learning techniques for H&E stain color normalization by utilizing public histological image datasets with varying colors for performance comparisons. The findings revealed that the non-negative matrix factorization techniques outperformed existing methods in the literature, particularly in feature preservation, achieving maximum FSIM, PSNR, QSSIM, and SSIM values of approximately 0.82, 40.21, 0.84, and 0.93, respectively. Furthermore, the impact of normalization on nuclear segmentation highlighted that the visual quality of the normalized images did not directly correlate with the quantitative segmentation results. Therefore, this study raises important open questions for the development of future research in this area.

Prompt engineering is essential for optimizing large language models (LLMs), yet the link between prompt structures and task performance remains underexplored. This work introduces an evolutionary approach that combines context-free grammar (CFG) with the MAP-Elites algorithm to systematically explore the prompt space. Our method prioritizes quality and diversity, generating high-performing and structurally varied prompts while analyzing their alignment with diverse tasks by varying traits such as the number of examples (shots) and reasoning depth. By systematically mapping the phenotypic space, we reveal how structural variations influence LLM performance, offering actionable insights for task-specific and adaptable prompt design. Evaluated on seven BigBench Lite tasks across multiple LLMs, our results underscore the critical interplay of quality and diversity, advancing the effectiveness and versatility of LLMs.

Covid-19 is a severe illness caused by the Sars-CoV-2 virus, initially identified in China in late 2019 and swiftly spreading globally. Since the virus primarily impacts the lungs, analyzing chest X-rays stands as a reliable and widely accessible means of diagnosing the infection. In computer vision, deep learning models such as CNNs have been the main adopted approach for detection of Covid-19 in chest X-ray images. However, we believe that handcrafted features can also provide relevant results, as shown previously in similar image classification challenges. In this study, we propose a method for identifying Covid-19 in chest X-ray images by extracting and classifying local and global percolation-based features. This technique was tested on three datasets: one comprising 2,002 segmented samples categorized into two groups (Covid-19 and Healthy); another with 1,125 non-segmented samples categorized into three groups (Covid-19, Healthy, and Pneumonia); and a third one composed of 4,809 non-segmented images representing three classes (Covid-19, Healthy, and Pneumonia). Then, 48 percolation features were extracted and give as input into six distinct classifiers. Subsequently, the AUC and accuracy metrics were assessed. We used the 10-fold cross-validation approach and evaluated lesion sub-types via binary and multiclass classification using the Hermite polynomial classifier, a novel approach in this domain. The Hermite polynomial classifier exhibited the most promising outcomes compared to five other machine learning algorithms, wherein the best obtained values for accuracy and AUC were 98.72% and 0.9917, respectively. We also evaluated the influence of noise in the features and in the classification accuracy. These results, based in the integration of percolation features with the Hermite polynomial, hold the potential for enhancing lesion detection and supporting clinicians in their diagnostic endeavors.

Clustering data using prior domain knowledge, starting from a partially labeled set, has recently been widely investigated. Often referred to as semi-supervised clustering, this approach leverages labeled data to enhance clustering accuracy. To maximize algorithm performance, it is crucial to ensure the safety of this prior knowledge. Methods addressing this concern are termed safe semi-supervised clustering (S3C) algorithms. This paper introduces the KNN graph-based safety-aware semi-supervised fuzzy c-means algorithm (K-GBS3FCM), which dynamically assesses neighborhood relationships between labeled and unlabeled data using the K-Nearest Neighbors (KNN) algorithm. This approach aims to optimize the use of labeled data while minimizing the adverse effects of incorrect labels. Additionally, it is proposed a mechanism that adjusts the influence of labeled data on unlabeled ones through regularization parameters and the average safety degree. Experimental results on multiple benchmark datasets demonstrate that the graph-based approach effectively leverages prior knowledge to enhance clustering accuracy. The proposed method was significantly superior in 64% of the 56 test configurations, obtaining higher levels of clustering accuracy when compared to other semi-supervised and traditional unsupervised methods. This research highlights the potential of integrating graph-based approaches, such as KNN, with established techniques to develop advanced clustering algorithms, offering significant applications in fields that rely on both labeled and unlabeled data for more effective clustering.

Early diagnosis of potentially malignant disorders, such as oral epithelial dysplasia (OED), is the most reliable way to prevent oral cancer. Computational algorithms have been used as a tool to aid specialists in this process. In recent years, CNN-based methods have gained more attention due to their improved results in nuclei segmentation tasks. Despite these relevant results, achieving high segmentation accuracy remains a challenging task. In this paper, we propose an ensemble of segmentation models to improve the performance of nuclei segmentation in OED histopathology images. The proposed ensemble consists of four CNN segmentation models, which were combined using three ensemble strategies: simple averaging, weighted averaging and majority voting, achieved accuracy of 90.69%, 90.70% and 88.49%, respectively, when applied to OED images. The model's performance was also evaluated on three publicly available datasets and achieved comparable performance to state-of-the-art segmentation methods. These values indicate that the proposed ensemble methods can be used in medical image analysis applications.

Liver cancer is one of the most common types of cancer according to World Health Statistics. Computer-aided diagnosis (CAD) systems are used in medical imaging for liver tumor identification and classification. Texture is a type of feature that can provide measurements of properties such as smoothness and regularity of the image. Handcraft techniques based on fractal geometry allow quantifying self-similarity properties present in images. However, new studies have shown that using information obtained from deep-learned feature maps can maximize the results of classical classifiers. This work presents an approach that investigates descriptors obtained by handcrafted and deep learning features, feature selection methods and the Hermite polynomial (HP) algorithm to classifier liver histological images. The results were evaluated using metrics such as accuracy (ACC) and the imbalance accuracy metric (IAM). The association with fractal features, Lasso regularization and the HP algorithm achieved 0.98 of IAM and 99.53% ACC, which was relevant when evaluated with other studies in the literature.

Oral epithelial dysplasia is a common type of pre-cancerous lesion that can be categorized as mild, moderate and severe. The manual diagnosis of this type of lesion is a time consuming and complex task. The use of digital systems applied to microscopic image analysis can aid the decision making of specialists. In recent years, deep learning-based methods are getting more attention due to its improved results in nuclei segmentation tasks. In this paper, we propose a methodology for nuclei segmentation on images of dysplastic tissues using neural networks. Several optimization algorithms and color normalization methods were evaluated. The methodology was performed on a dataset of mice tongue images. The experimental evaluations showed that the Nadam optimizer in combination with images without the use of color normalization obtained the best results. The method was able to segment the images with an average accuracy of 0.887, the sensitivity of 0.762 and specificity of 0.942. The algorithm was compared to other segmentation methods and showed relevant results. These values indicate that the proposed method can be used as a tool to aid specialists in the nuclei analysis of histological images of the buccal cavity.

The analysis of histological image features for automatic detection of pathologies plays an important role in medicine. Considering that, we proposed a method based on the association of features extracted by multi-scale and multidimensional fractal techniques, Haralick descriptors, and CNN for pattern recognition of colorectal cancer, breast cancer, and non-Hodgkin lymphomas. For feature selection, we applied the ReliefF algorithm to rank the best 50 features and then applied the evolutionary algorithms GWO, PSO, and GA. The classification was made with SVM, K*, and Random Forest algorithms. This strategy allows classifying plenty of feature vectors selected by different algorithms, and consequently, improves the accuracy of the interpretations about the class distinction of histological images. The best combination found was composed of GA and K* algorithms, resulting in 91.06%, 90.52% e 82.01% accuracy for colorectal cancer, breast cancer, and non-Hodgkin lymphomas respectively. The performance obtained by the method indicates that the feature association extracted by different approaches and their subsequent selection and classification presents a potential field for further studies with a high degree of contribution to science.

Fracta1 techniques are widely explored to quantity and recognize texture patterns in digital images. Among the different types of fractal techniques, one that stands out is the Higuchi fractal dimension. The Higuchi fractal dimension allows determining how much space is filled in a two-dimensional image through the projection of the image in 1D signals. This property provides the possibility to calculate the Higuchi fractal dimension of specific regions of an image. The main drawback of this technique is that it does not allow the analysis of color images. In this work, a new Higuchi fractal dimension model is presented with the inclusion of multidimensional and multiscale strategies to expand the traditional Higuchi dimension for texture analysis in color images. The multidimensional approach was applied considering each pixel of the color image as an n-dimensional vector. The multiscale strategy was defined using different scales of observation. The proposed model was applied to a set of 151 colorectal histological images to test its ability to quantity and separate the benign and malignant groups from colorectal cancer. The performance of the proposed model was compared with that provided by consolidated fractal dimension techniques. The results obtained are promising and indicate that the proposal contributes significantly to the literature focused on the quantification and recognition of texture patterns with fractal techniques.

Histological images analysis is widely used to carry out diagnoses of different types of cancer. Digital image processing methods can be used for this purpose, leading to more objective diagnoses. Segmentation techniques are applied to identify cellular structures indicative of diseases. In addition, the extracted features from these specific regions can aid pathologists in diagnoses decision using classification techniques. In this paper, we present an evaluation of evolutionary algorithms applied to lymphoma images for segmentation of their neoplastic cellular nuclei. In a second stage, we investigated the performance of the segmented images in the classification step. Initially, the R channel from RGB color model was processed with histogram equalization and Gaussian filter. In the segmentation step, optimization methods were analyzed in combination with the fuzzy 3-partition technique. Then, we also applied the valley-emphasis method and morphological operations to remove false positive regions in the post-processing step. Intensity and texture features were extracted and classified by the support vector machine method for diagnoses of 62 and 99 images of follicular lymphoma and mantle cell lymphoma, respectively. The results were evaluated through qualitative and quantitative analyses and the differential evolution method has reached the best results in the segmentation step. This technique allowed a relevant performance on the classification task with a mean value of accuracy of 99.38%.

Non-Hodgkin lymphoma is the most common cancer of the lymphatic system and should be considered as a group of several closely related cancers, which can show differences in their growth patterns, their impact on the body and how they are treated. The diagnosis of the different types of neoplasia is made by a specialist through the analysis of histological images. However, these analyses are complex and the same case can lead to different understandings among pathologists, due to the exhaustive analysis of decisions, the time required and the presence of complex histological features. In this context, computational algorithms can be applied as tools to aid specialists through the application of segmentation methods to identify regions of interest that are essential for lymphomas diagnosis. In this paper, an unsupervised method for segmentation of nuclear components of neoplastic cells is proposed to analyze histological images of lymphoma stained with hematoxylin-eosin. The proposed method is based on the association among histogram equalization, Gaussian filter, fuzzy 3-partition entropy, genetic algorithm, morphological techniques and the valley-emphasis method in order to analyze neoplastic nuclear components, improve the contrast and illumination conditions, remove noise, split overlapping cells and refine contours. The results were evaluated through comparisons with those provided by a specialist and techniques available in the literature considering the metrics of accuracy, sensitivity, specificity and variation of information. The mean value of accuracy for the proposed method was 81.48%. Although the method obtained sensitivity rates between 41% and 51%, the accuracy values showed relevance when compared to those provided by other studies. Therefore, the novelties presented here may already encourage new studies with a more comprehensive overview of lymphoma segmentation.

Simulations based on the Finite Element Method are widely applied in different contexts. The convergence and reliability of results depends directly on the quality of the tetrahedrons that compose FEM meshes. In this context, this work aimed to obtain tetrahedral meshes of real structures from tomographic images using open source software and split those tetrahedrons that do not contribute for convergence of such simulations through the aspect ratio criteria. As a result, tetrahedral meshes from real structures were obtained together with indications of the regions that could impair FEM simulations. Moreover, this approach makes it possible to study specific methods for local refinements for improving numerical simulations. These findings are of great interest to users generally, and particularly to researchers working on geometric and topological methods for shape and solid modeling, with extensive applications in the fields of Computational Fluid Dynamics, Heat Transfer and others.

The radiofrequency cardiac ablation is a minimal invasive surgical procedure used for treating tachycardia, atrial fibrillation and atrial flutter. A possible complication is esophageal injury: the union of tissues from left atrium and esophagus, through necrosis. While this operation is being made, it is necessary to monitor the tissues temperatures accurately. The tests needed are complex and imply in death risks of the patient. Researchers are directed to simulate, with the finite element method, the behavior of the tissues under the influence of different levels of temperature: the objective is improving the cardiac ablation. Computational strategies were described in this work in order to obtain integrated meshes from thoracic and abdominal structures which are relevant to the study of this procedure. The methodology was based on strategies to represent structures in different scale and integrate software packages to generate meshes. The results were integrated, multiscale, and highly refined tetrahedral meshes from thoracic structures. The models were evaluated from dihedral angles histograms, indicating values between 5 and 170 degrees.

Blood smear image analysis is essential to correlate the amount of leukocytes in these images with malignancies such as the leukemias. Techniques of digital image processing can be used to aid pathologists in this analysis, leading to appropriate treatments for the patient. This paper presents an unsupervised segmentation method for the nuclear structures in leukocytes. Deconvolution was used to split the Giemsa stain components and the regions of interest were selected using a thresholding algorithm called Neighborhood Valley-emphasis. A postprocessing approach based on morphological operators was applied in these detected structures. The proposed algorithm was tested on 367 images containing leukocytes and other blood structures. A performance analysis was conducted through the Jaccard and accuracy metrics featuring results of 89.89% and 99.57%, respectively. Such results were compared to other published articles and this was considered the most promising method.

This work proposes to describe a teaching approach for introductory laboratory course in object-oriented programming and its respective teacher's experience. The profile of the first classes of freshmen, with different career goals, enrolled in Interdisciplinary Bachelor of Science and Technology at Federal University of ABC is presented. Educational methodology adopted in the lab using tutorials and a project-based learning approach is also discussed. Furthermore, some statistics about assessment of a student class in response to the learning activities, and its respective analysis are shown. And finally their evaluation about this educational approach is presented.