PRZEGLĄD METOD SELEKCJI CECH UŻYWANYCH W DIAGNOSTYCE CZERNIAKA

Currently, a large number of trait selection methods are used. They are becoming more and more of interest among researchers. Some of the methods are of course used more frequently. The article describes the basics of selection-based algorithms. FS methods fall into three categories: filter wrappers, embedded methods. Particular attention was paid to finding examples of applications of the described methods in the diagnosisof skin melanoma.Obecnie stosuje się wiele metod selekcji cech. Cieszą się coraz większym zainteresowaniem badaczy. Oczywiście niektóre metody są stosowane częściej. W artykule zostały opisane podstawy działania algorytmów opartych na selekcji. Metody selekcji cech należące dzielą się na trzy kategorie: metody filtrowe, metody opakowujące, metody wbudowane. Zwrócono szczególnie uwagę na znalezienie przykładów zastosowań opisanych metod w diagnostyce czerniaka skóry

Knowledge-Guided Data-Centric AI in Healthcare: Progress, Shortcomings, and Future Directions

The success of deep learning is largely due to the availability of large amounts of training data that cover a wide range of examples of a particular concept or meaning. In the field of medicine, having a diverse set of training data on a particular disease can lead to the development of a model that is able to accurately predict the disease. However, despite the potential benefits, there have not been significant advances in image-based diagnosis due to a lack of high-quality annotated data. This article highlights the importance of using a data-centric approach to improve the quality of data representations, particularly in cases where the available data is limited. To address this "small-data" issue, we discuss four methods for generating and aggregating training data: data augmentation, transfer learning, federated learning, and GANs (generative adversarial networks). We also propose the use of knowledge-guided GANs to incorporate domain knowledge in the training data generation process. With the recent progress in large pre-trained language models, we believe it is possible to acquire high-quality knowledge that can be used to improve the effectiveness of knowledge-guided generative methods.Comment: 21 pages, 13 figures, 4 table

Understanding from Machine Learning Models

Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide understanding misguided? In this paper, using the case of deep neural networks, I argue that it is not the complexity or black box nature of a model that limits how much understanding the model provides. Instead, it is a lack of scientific and empirical evidence supporting the link that connects a model to the target phenomenon that primarily prohibits understanding

Leveraging Computer Vision for Applications in Biomedicine and Geoscience

Skin cancer is one of the most common types of cancer and is usually classified as either non-melanoma and melanoma skin cancer. Melanoma skin cancer accounts for about half of all skin cancer-related deaths. The 5-year survival rate is 99% when the cancer is detected early but drops to 25% once it becomes metastatic. In other words, the key to preventing death is early detection. Foraminifera are microscopic single-celled organisms that exist in marine environments and are classified as living a benthic or planktic lifestyle. In total, roughly 50,000 species are known to have existed, of which about 9,000 are still living today. Foraminifera are important proxies for reconstructing past ocean and climate conditions and as bio-indicators of anthropogenic pollution. Since the 1800s, the identification and counting of foraminifera have been performed manually. The process is resource-intensive. In this dissertation, we leverage recent advances in computer vision, driven by breakthroughs in deep learning methodologies and scale-space theory, to make progress towards both early detection of melanoma skin cancer and automation of the identification and counting of microscopic foraminifera. First, we investigate the use of hyperspectral images in skin cancer detection by performing a critical review of relevant, peer-reviewed research. Second, we present a novel scale-space methodology for detecting changes in hyperspectral images. Third, we develop a deep learning model for classifying microscopic foraminifera. Finally, we present a deep learning model for instance segmentation of microscopic foraminifera. The works presented in this dissertation are valuable contributions in the fields of biomedicine and geoscience, more specifically, towards the challenges of early detection of melanoma skin cancer and automation of the identification, counting, and picking of microscopic foraminifera