Class imbalance ensemble learning based on the margin theory

The proportion of instances belonging to each class in a data-set plays an important role in machine learning. However, the real world data often suffer from class imbalance. Dealing with multi-class tasks with different misclassification costs of classes is harder than dealing with two-class ones. Undersampling and oversampling are two of the most popular data preprocessing techniques dealing with imbalanced data-sets. Ensemble classifiers have been shown to be more effective than data sampling techniques to enhance the classification performance of imbalanced data. Moreover, the combination of ensemble learning with sampling methods to tackle the class imbalance problem has led to several proposals in the literature, with positive results. The ensemble margin is a fundamental concept in ensemble learning. Several studies have shown that the generalization performance of an ensemble classifier is related to the distribution of its margins on the training examples. In this paper, we propose a novel ensemble margin based algorithm, which handles imbalanced classification by employing more low margin examples which are more informative than high margin samples. This algorithm combines ensemble learning with undersampling, but instead of balancing classes randomly such as UnderBagging, our method pays attention to constructing higher quality balanced sets for each base classifier. In order to demonstrate the effectiveness of the proposed method in handling class imbalanced data, UnderBagging and SMOTEBagging are used in a comparative analysis. In addition, we also compare the performances of different ensemble margin definitions, including both supervised and unsupervised margins, in class imbalance learning

Deep Learning Classification of Deep Ultraviolet Fluorescence Images for Margin Assessment During Breast Cancer Surgery

Breast-conserving surgery (BCS) is a widely used treatment for breast cancer, but ensuring the complete removal of cancer cells from the surgical margins remains a challenge. Deep ultraviolet (DUV) fluorescence scanning microscopy offers a potential solution by providing real-time whole-surface imaging of resected tissues during BCS. However, interpreting DUV images for margin assessment requires an automated classification method. This dissertation addresses this need by proposing a deep learning-based classification approach for DUV fluorescence images in intra-operative margin assessment of breast cancer.To overcome the limited availability of DUV image datasets and potential over- fitting, the study combines patch-level classification using transfer learning with regional importance maps generated through the Grad-CAM++ algorithm. The proposed method- ology involves dividing DUV whole-slide images into smaller patches, converting them to grayscale, and analyzing pixel values to identify valid patches. A pre-trained ResNet50 network and an XGBoost classifier are utilized for patch-level classification, while Grad- CAM++ generates regional importance maps for the entire DUV image. The decision fusion method combines patch-level classification labels and regional importance maps to determine the whole-slide image (WSI)-level classification label by calculating the total number of malignant patches and comparing it to a threshold percent- age of total foreground patches. A binary classification is obtained for the entire WSI. The proposed methodology is implemented using PyTorch and a dataset consisting of 60 DUV images of breast tissue samples. The DUV images were obtained using a custom DUV-Fluorescence Scanning Microscopy system, which provided high-resolution images with fluorescence staining for accurate tissue classification. The results of this study contribute to the field of intra-operative margin assessment in breast cancer by demonstrating the effectiveness of deep learning-based classification of DUV images. The combination of transfer learning, regional importance maps, and deci- sion fusion provides a robust approach for accurately classifying breast tissue as malignant or normal/benign. This research opens new avenues for utilizing deep learning techniques in DUV fluorescence imaging and has the potential to improve surgical outcomes in breast- conserving surgery

Gene Expression Analysis Methods on Microarray Data a A Review

In recent years a new type of experiments are changing the way that biologists and other specialists analyze many problems. These are called high throughput experiments and the main difference with those that were performed some years ago is mainly in the quantity of the data obtained from them. Thanks to the technology known generically as microarrays, it is possible to study nowadays in a single experiment the behavior of all the genes of an organism under different conditions. The data generated by these experiments may consist from thousands to millions of variables and they pose many challenges to the scientists who have to analyze them. Many of these are of statistical nature and will be the center of this review. There are many types of microarrays which have been developed to answer different biological questions and some of them will be explained later. For the sake of simplicity we start with the most well known ones: expression microarrays