Deep Active Learning for Automatic Mitotic Cell Detection on HEp-2 Specimen Medical Images

Identifying Human Epithelial Type 2 (HEp-2) mitotic cells is a crucial procedure in anti-nuclear antibodies (ANAs) testing, which is the standard protocol for detecting connective tissue diseases (CTD). Due to the low throughput and labor-subjectivity of the ANAs' manual screening test, there is a need to develop a reliable HEp-2 computer-aided diagnosis (CAD) system. The automatic detection of mitotic cells from the microscopic HEp-2 specimen images is an essential step to support the diagnosis process and enhance the throughput of this test. This work proposes a deep active learning (DAL) approach to overcoming the cell labeling challenge. Moreover, deep learning detectors are tailored to automatically identify the mitotic cells directly in the entire microscopic HEp-2 specimen images, avoiding the segmentation step. The proposed framework is validated using the I3A Task-2 dataset over 5-fold cross-validation trials. Using the YOLO predictor, promising mitotic cell prediction results are achieved with an average of 90.011% recall, 88.307% precision, and 81.531% mAP. Whereas, average scores of 86.986% recall, 85.282% precision, and 78.506% mAP are obtained using the Faster R-CNN predictor. Employing the DAL method over four labeling rounds effectively enhances the accuracy of the data annotation, and hence, improves the prediction performance. The proposed framework could be practically applicable to support medical personnel in making rapid and accurate decisions about the mitotic cells' existence

Los esfuerzos para dirección de bosque de alcornoque y sus efectos sobre conservación de suelo, la meseta Shoul, región de Rabat, Marruecos

The Shoul oak grove is a forested ecosystem inherited from a Holocene phase of ecological optimum; its evolution, through the double geologic and human temporality, and in relation with several processes of degradation, led to the progressive loss of its environmental equilibrium and further to the reduction of its economic contributions. The fragility of these forests is the consequence of the convergence of two main factors, i) the intrinsic fragility of the forested environment based on an unstable balance between the tree, the leached soils and their moisture content ; ii) the anthropological action on the forest environment and its degradation with the change of its floristic composition. During the colonization the new context was at the origin of the new social and economic relation between the forest and the surrounding populations. The current use of this oak grove is in a classic scheme of the reports society / forest in Morocco. The population is especially of pastoral main activity in the bordering communes. But the oak groves of Mamora-Sehoul are integrated into the area of influence of several cities, what exposes the forest to the risks of uncontrolled urbanization. These oak groves are thus in the centre of interest of several stakeholders with opposite behaviour and a new paradigm of relation rural/urban. Through a double approach, environmental and socio-economic, this paper will try to bring elements of answer by analyzing the interactions between a forest which reached an alarming threshold of degradation and a society affected by important changes in its modes of intervention and exploitation.La arboleda de roble Shoul es un ecosistema arbolado heredado de una fase de Holocene de grado óptimo ecológico; su evolución, por la doble temporalidad geológica y humana, y en relación con varios procesos de degradación, conducida a la pérdida progresiva de su equilibrio ambiental y con relación a la reducción de sus contribuciones económicas. La fragilidad de estos bosques es la consecuencia de la convergencia de dos factores principales, i) la fragilidad intrínseca del ambiente arbolado basado en un equilibrio(saldo) inestable entre el árbol, los suelos leached y su contenido de humedad; ii) la acción antropológica sobre el ambiente forestal y su degradación con el cambio de su composición floristic. Durante la colonización el nuevo contexto estaba en el origen de la nueva relación social y económica entre el bosque y las poblaciones circundantes. El empleo corriente de esta arboleda de roble está en un esquema clásico de la sociedad de informes / el bosque en Marruecos. La población es sobre todo de actividad pastoral principal en las comunas de lindar. Pero las arboledas de roble de Mamora-Sehoul son integradas en el área de influencia de varias ciudades, que expone el bosque a los riesgos de urbanización incontrolada. Estas arboledas de roble son así en el centro de interés de varios tenedores de apuestas con el comportamiento de enfrente y un nuevo paradigma de relación rural/urbana. Por un doble acercamiento, ambiental y socioeconómico, este papel(periódico) tratará de traer los elementos de respuesta por analizando las interacciones entre un bosque que alcanzó un umbral alarmante de degradación y una sociedad afectada por cambios importantes de sus modos de intervención y explotación

Automatic Detection of Malignant Masses in Digital Mammograms Based on a MCET-HHO Approach

Digital image processing techniques have become an important process within medical images. These techniques allow the improvement of the images in order to facilitate their interpretation for specialists. Within these are the segmentation methods, which help to divide the images by regions based on different approaches, in order to identify details that may be complex to distinguish initially. In this work, it is proposed the implementation of a multilevel threshold segmentation technique applied to mammography images, based on the Harris Hawks Optimization (HHO) algorithm, in order to identify regions of interest (ROIs) that contain malignant masses. The method of minimum cross entropy thresholding (MCET) is used to select the optimal threshold values for the segmentation. For the development of this work, four mammography images were used (all with presence of a malignant tumor), in their two views, craniocaudal (CC) and mediolateral oblique (MLO), obtained from the Digital Database for Screening Mammography (DDSM). Finally, the ROIs calculated were compared with the original ROIs of the database through a series of metrics, to evaluate the behavior of the algorithm. According to the results obtained, where it is shown that the agreement between the original ROIs and the calculated ROIs is significantly high, it is possible to conclude that the proposal of the MCET-HHO algorithm allows the automatic identification of ROIs containing malignant tumors in mammography images with significant accuracy.Digital image processing techniques have become an important process within medical images. These techniques allow the improvement of the images in order to facilitate their interpretation for specialists. Within these are the segmentation methods, which help to divide the images by regions based on different approaches, in order to identify details that may be complex to distinguish initially. In this work, it is proposed the implementation of a multilevel threshold segmentation technique applied to mammography images, based on the Harris Hawks Optimization (HHO) algorithm, in order to identify regions of interest (ROIs) that contain malignant masses. The method of minimum cross entropy thresholding (MCET) is used to select the optimal threshold values for the segmentation. For the development of this work, four mammography images were used (all with presence of a malignant tumor), in their two views, craniocaudal (CC) and mediolateral oblique (MLO), obtained from the Digital Database for Screening Mammography (DDSM). Finally, the ROIs calculated were compared with the original ROIs of the database through a series of metrics, to evaluate the behavior of the algorithm. According to the results obtained, where it is shown that the agreement between the original ROIs and the calculated ROIs is significantly high, it is possible to conclude that the proposal of the MCET-HHO algorithm allows the automatic identification of ROIs containing malignant tumors in mammography images with significant accuracy

An endemic flora of dispersed spores from the Middle Devonian of Iberia

Diverse assemblages of dispersed spores have been recovered from Middle Devonian rocks in northern Spain, revealing a significant endemism in the flora. Middle Devonian Iberia was part of a relatively isolated island complex (Armorican Terrane Assemblage), separated by considerable tracts of ocean from Laurussia to the north‐west and Gondwana to the south‐east. The Middle Devonian deposits of the Cantabrian Zone of northern Spain are entirely marine and comprise a thick clastic unit sandwiched between extensive carbonate units. The clastic unit, the laterally equivalent Naranco, Huergas and Gustalapiedra formations of Asturias, León and Palencia provinces, represents a nearshore‐offshore transect across a marine shelf. This unit is also believed to encompass the Kačák Event, an important global extinction event. The recovered palynomorphs include marine (phytoplankton, chitinozoans, scolecodonts) and terrestrial (spores) assemblages. These are abundant and well preserved, although of variable thermal maturity. Here, we describe the dispersed spores and consider their significance as regards biostratigraphy, palaeophytogeography and Kačák Event interpretation. The dispersed spores represent a single assemblage assignable to the lemurata–langii Assemblage Zone (lemurata Subzone) indicating a probable early (but not earliest) Givetian age. Signs of endemism include various taxa known only from this region, some taxa appearing to have discordant ranges compared with elsewhere, and the absence from Iberia of certain prominent taxa characteristic of coeval assemblages elsewhere, such as those with grapnel‐tipped processes. The abrupt interruption of carbonate deposition, with a change to rapid deposition of thick clastic deposits, provides support for a monsoonal cause of the Kačák Event

Artificial Intelligence for Medical Diagnostics—Existing and Future AI Technology!

We would like to express our gratitude to all authors who contributed to the Special Issue of “Artificial Intelligence Advances for Medical Computer-Aided Diagnosis” by providing their excellent and recent research findings for AI-based medical diagnosis [...

Dual-Sensor Signals Based Exact Gaussian Process-Assisted Hybrid Feature Extraction and Weighted Feature Fusion for Respiratory Rate and Uncertainty Estimations

Accurately estimating respiratory rate (RR) has become essential for patients and the elderly. Hence, we propose a novel method that uses exact Gaussian process regression (EGPR)-assisted hybrid feature extraction and feature fusion based on photoplethysmography and electrocardiogram signals to improve the reliability of accurate RR and uncertainty estimations. First, we obtain the power spectral features and use the multi-phase feature model to compensate for insufficient input data. Then, we combine four different feature sets and choose features with high weights using a robust neighbor component analysis. The proposed EGPR algorithm provides a confidence interval representing the uncertainty. Therefore, the proposed EGPR algorithm, including hybrid feature extraction and weighted feature fusion, is an excellent model with improved reliability for accurate RR estimation. Furthermore, the proposed EGPR methodology is likely the only one currently available that provides highly stable variation and confidence intervals. The proposed EGPR-MF, 0.993 breath per minute (bpm), and EGPR-feature fusion, 1.064 (bpm), show the lowest mean absolute error compared to the other models

ArCAR: A Novel Deep Learning Computer-Aided Recognition for Character-Level Arabic Text Representation and Recognition

Arabic text classification is a process to simultaneously categorize the different contextual Arabic contents into a proper category. In this paper, a novel deep learning Arabic text computer-aided recognition (ArCAR) is proposed to represent and recognize Arabic text at the character level. The input Arabic text is quantized in the form of 1D vectors for each Arabic character to represent a 2D array for the ArCAR system. The ArCAR system is validated over 5-fold cross-validation tests for two applications: Arabic text document classification and Arabic sentiment analysis. For document classification, the ArCAR system achieves the best performance using the Alarabiya-balance dataset in terms of overall accuracy, recall, precision, and F1-score by 97.76%, 94.08%, 94.16%, and 94.09%, respectively. Meanwhile, the ArCAR performs well for Arabic sentiment analysis, achieving the best performance using the hotel Arabic reviews dataset (HARD) balance dataset in terms of overall accuracy and F1-score by 93.58% and 93.23%, respectively. The proposed ArCAR seems to provide a practical solution for accurate Arabic text representation, understanding, and classification

A Novel Deep Learning ArCAR System for Arabic Text Recognition with Character-Level Representation

AI-based text classification is a process to classify Arabic contents into their categories. With the increasing number of Arabic texts in our social life, traditional machine learning approaches are facing different challenges due to the complexity of the morphology and the delicate variation of the Arabic language. This work proposes a model to represent and recognize Arabic text at the character level based on the capability of a deep convolutional neural network (CNN). This system was validated using five-fold cross-validation tests for Arabic text document classification. We have used our proposed system to evaluate Arabic text. The ArCAR system shows its capability to classify Arabic text in character-level. For document classification, the ArCAR system achieves the best performance using the AlKhaleej-balance dataset in terms of accuracy equal to 97.76%. The proposed ArCAR seems to provide a practical solution for accurate Arabic text representation, both for understanding and as a classifications system

ETECADx: Ensemble Self-Attention Transformer Encoder for Breast Cancer Diagnosis Using Full-Field Digital X-ray Breast Images

Early detection of breast cancer is an essential procedure to reduce the mortality rate among women. In this paper, a new AI-based computer-aided diagnosis (CAD) framework called ETECADx is proposed by fusing the benefits of both ensemble transfer learning of the convolutional neural networks as well as the self-attention mechanism of vision transformer encoder (ViT). The accurate and precious high-level deep features are generated via the backbone ensemble network, while the transformer encoder is used to diagnose the breast cancer probabilities in two approaches: Approach A (i.e., binary classification) and Approach B (i.e., multi-classification). To build the proposed CAD system, the benchmark public multi-class INbreast dataset is used. Meanwhile, private real breast cancer images are collected and annotated by expert radiologists to validate the prediction performance of the proposed ETECADx framework. The promising evaluation results are achieved using the INbreast mammograms with overall accuracies of 98.58% and 97.87% for the binary and multi-class approaches, respectively. Compared with the individual backbone networks, the proposed ensemble learning model improves the breast cancer prediction performance by 6.6% for binary and 4.6% for multi-class approaches. The proposed hybrid ETECADx shows further prediction improvement when the ViT-based ensemble backbone network is used by 8.1% and 6.2% for binary and multi-class diagnosis, respectively. For validation purposes using the real breast images, the proposed CAD system provides encouraging prediction accuracies of 97.16% for binary and 89.40% for multi-class approaches. The ETECADx has a capability to predict the breast lesions for a single mammogram in an average of 0.048 s. Such promising performance could be useful and helpful to assist the practical CAD framework applications providing a second supporting opinion of distinguishing various breast cancer malignancies

ETECADx: Ensemble Self-Attention Transformer Encoder for Breast Cancer Diagnosis Using Full-Field Digital X-ray Breast Images

Early detection of breast cancer is an essential procedure to reduce the mortality rate among women. In this paper, a new AI-based computer-aided diagnosis (CAD) framework called ETECADx is proposed by fusing the benefits of both ensemble transfer learning of the convolutional neural networks as well as the self-attention mechanism of vision transformer encoder (ViT). The accurate and precious high-level deep features are generated via the backbone ensemble network, while the transformer encoder is used to diagnose the breast cancer probabilities in two approaches: Approach A (i.e., binary classification) and Approach B (i.e., multi-classification). To build the proposed CAD system, the benchmark public multi-class INbreast dataset is used. Meanwhile, private real breast cancer images are collected and annotated by expert radiologists to validate the prediction performance of the proposed ETECADx framework. The promising evaluation results are achieved using the INbreast mammograms with overall accuracies of 98.58% and 97.87% for the binary and multi-class approaches, respectively. Compared with the individual backbone networks, the proposed ensemble learning model improves the breast cancer prediction performance by 6.6% for binary and 4.6% for multi-class approaches. The proposed hybrid ETECADx shows further prediction improvement when the ViT-based ensemble backbone network is used by 8.1% and 6.2% for binary and multi-class diagnosis, respectively. For validation purposes using the real breast images, the proposed CAD system provides encouraging prediction accuracies of 97.16% for binary and 89.40% for multi-class approaches. The ETECADx has a capability to predict the breast lesions for a single mammogram in an average of 0.048 s. Such promising performance could be useful and helpful to assist the practical CAD framework applications providing a second supporting opinion of distinguishing various breast cancer malignancies