Reception of the Herzog Stjepan Vukcic Kosaca in Herzegovina in the Second Half of the 20th Century

U radu se nastoji prikazati recepcija hercega Stjepana Vukčića Kosače u Hercegovini u drugoj polovici 20. stoljeća s posebnim naglaskom na razdoblje u kojemu je Bosna i Hercegovina bila jedna od republika bivše Jugoslavije. S obzirom na činjenicu da bi se barem početno znanje o hercegu Stjepanu trebalo steći tijekom osnovnoga i srednjoškolskoga obrazovanja, najprije je dat osvrt na nastavne planove i programe u kojima su, između ostaloga, naznačeni ciljevi (zadatci) nastave povijesti, a zatim se analiziraju tekstovi u udžbenicima povijesti koji su bili u uporabi u bh. školama (osnovnim i gimnazijama), u kojima se u određenim nastavnim jedinicama obrađuje djelovanje hercega Stjepana. Sudeći prema nastavnim planovima i programima, kao i sadržaju udžbenika iz druge polovice 20. stoljeća (barem nama dostupnima), mladež u Hercegovini u razmatranom razdoblju nije mogla mnogo saznati i naučiti o ovoj povijesnoj osobi, s čijom je titulom povezano ime prostora u kojemu žive. Na kraju istražuje se hercegova prisutnost u nazivima ulica, trgova i institucija u hercegovačkim gradovima i općinama. Istraživanje pokazuje da je tek u novije vrijeme evidentan interes nekih pojedinaca i institucija koje žele ovoj povijesnoj osobi dati značenje koje joj pripada. Naime, sve ulice u Hercegovini koje nose njegovo ime, kao i jedna institucija, imenovane su nakon 1990. godine.With the aim of presenting the reception of the Herzog Stjepan Vukcic Kosaca in Hercegovina in the second half of the 20th century, the paper analyzes the contents of history textbooks that write about the life and work of the Herzog Stjepan and his presence in the names of streets, squares and institutions in Herzegovinian towns and municipalities. Considering the second half of the 20th century, most attention is paid to the period in which Bosnia and Herzegovina was one of the republics of the former Yugoslavia. To illustrate the contents from medieval history, or the ones related to Stjepan, most important are school curricula, in which, apart from the teaching units and lessons, the teaching of history was particularly indicative, when during this period the constant task was "spreading fraternity and unity" or "developing patriotic awareness among students", the emphasis being on teaching history of the "newer period". Judging by the curricula and the content of textbooks from the second half of the 20th century (at least those available to us), the youth of Herzegovina in the mentioned period could not learn much about this historical person whose name is associated with the name of the area in which they live. Namely, by looking at the texts of the textbooks up to the 90s, it is evident that the texts in the textbooks differ, both in scope and emphasis on certain facts. What all the authors mention is taking of the \u27Herzog\u27 title in 1448 and the fall of Herzegovina under the Ottoman rule, whereas all the other facts about the Herzog Stjepan vary from one text to another. As there were no official textbooks in the region of Herzegovina in the 90s of the 20th century, the paper, as an example, presents the texts only from two textbooks (one for elementary school and the other for grammar school) used in some schools in Herzegovina which worked according to the Croatian curriculum, where we can find hardly any information about the Herzog Stjepan. Finally, the results of the research of the Herzog\u27s presence in the names of streets, squares and institutions of the Herzegovinian cities and municipalities show that the interest of some individuals and institutions, who want to give this historical person the significance he deserves, has only recently become evident. Namely, all the streets in Herzegovina named after the Herzog, as well as one institution, got this name after 1990

Assessment of immunological features in muscle-invasive bladder cancer prognosis using ensemble learning

Funding: This research received financial support from Definiens GmbH and the Industrial Centre for AI Research in digital Diagnostics (iCAIRD) which is funded by Innovate UK on behalf of UK Research and Innovation (UKRI) [project number: 104690].The clinical staging and prognosis of muscle-invasive bladder cancer (MIBC) routinely includes the assessment of patient tissue samples by a pathologist. Recent studies corroborate the importance of image analysis in identifying and quantifying immunological markers from tissue samples that can provide further insight into patient prognosis. In this paper, we apply multiplex immunofluorescence to MIBC tissue sections to capture whole-slide images and quantify potential prognostic markers related to lymphocytes, macrophages, tumour buds, and PD-L1. We propose a machine-learning-based approach for the prediction of 5 year prognosis with different combinations of image, clinical, and spatial features. An ensemble model comprising several functionally different models successfully stratifies MIBC patients into two risk groups with high statistical significance (p value < 1×10−5). Critical to improving MIBC survival rates, our method correctly classifies 71.4% of the patients who succumb to MIBC, which is significantly more than the 28.6% of the current clinical gold standard, the TNM staging system.Publisher PDFPeer reviewe

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.Comment: Survey, 41 page

Nucleus segmentation : towards automated solutions

Single nucleus segmentation is a frequent challenge of microscopy image processing, since it is the first step of many quantitative data analysis pipelines. The quality of tracking single cells, extracting features or classifying cellular phenotypes strongly depends on segmentation accuracy. Worldwide competitions have been held, aiming to improve segmentation, and recent years have definitely brought significant improvements: large annotated datasets are now freely available, several 2D segmentation strategies have been extended to 3D, and deep learning approaches have increased accuracy. However, even today, no generally accepted solution and benchmarking platform exist. We review the most recent single-cell segmentation tools, and provide an interactive method browser to select the most appropriate solution.Peer reviewe

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

Automated tumour budding quantification by machine learning augments TNM staging in muscle-invasive bladder cancer prognosis

Tumour budding has been described as an independent prognostic feature in several tumour types. We report for the first time the relationship between tumour budding and survival evaluated in patients with muscle invasive bladder cancer. A machine learning-based methodology was applied to accurately quantify tumour buds across immunofluorescence labelled whole slide images from 100 muscle invasive bladder cancer patients. Furthermore, tumour budding was found to be correlated to TNM (p = 0.00089) and pT (p = 0.0078) staging. A novel classification and regression tree model was constructed to stratify all stage II, III, and IV patients into three new staging criteria based on disease specific survival. For the stratification of non-metastatic patients into high or low risk of disease specific death, our decision tree model reported that tumour budding was the most significant feature (HR = 2.59, p = 0.0091), and no clinical feature was utilised to categorise these patients. Our findings demonstrate that tumour budding, quantified using automated image analysis provides prognostic value for muscle invasive bladder cancer patients and a better model fit than TNM staging.Publisher PDFPeer reviewe

Local and deep texture features for classification of natural and biomedical images

Developing efficient feature descriptors is very important in many computer vision applications including biomedical image analysis. In the past two decades and before the popularity of deep learning approaches in image classification, texture features proved to be very effective to capture the gradient variation in the image. Following the success of the Local Binary Pattern (LBP) descriptor, many variations of this descriptor were introduced to further improve the ability of obtaining good classification results. However, the problem of image classification gets more complicated when the number of images increases as well as the number of classes. In this case, more robust approaches must be used to address this problem. In this thesis, we address the problem of analyzing biomedical images by using a combination of local and deep features. First, we propose a novel descriptor that is based on the motif Peano scan concept called Joint Motif Labels (JML). After that, we combine the features extracted from the JML descriptor with two other descriptors called Rotation Invariant Co-occurrence among Local Binary Patterns (RIC-LBP) and Joint Adaptive Medina Binary Patterns (JAMBP). In addition, we construct another descriptor called Motif Patterns encoded by RIC-LBP and use it in our classification framework. We enrich the performance of our framework by combining these local descriptors with features extracted from a pre-trained deep network called VGG-19. Hence, the 4096 features of the Fully Connected 'fc7' layer are extracted and combined with the proposed local descriptors. Finally, we show that Random Forests (RF) classifier can be used to obtain superior performance in the field of biomedical image analysis. Testing was performed on two standard biomedical datasets and another three standard texture datasets. Results show that our framework can beat state-of-the-art accuracy on the biomedical image analysis and the combination of local features produce promising results on the standard texture datasets.Includes bibliographical reference