Pathologist-Like Explanations Unveiled: an Explainable Deep Learning System for White Blood Cell Classification

White blood cells (WBCs) play a crucial role in safeguarding the human body against pathogens and foreign substances. Leveraging the abundance of WBC imaging data and the power of deep learning algorithms, automated WBC analysis has the potential for remarkable accuracy. However, the capability of deep learning models to explain their WBC classification remains largely unexplored. In this study, we introduce HemaX, an explainable deep neural network-based model that produces pathologist-like explanations using five attributes: granularity, cytoplasm color, nucleus shape, size relative to red blood cells, and nucleus to cytoplasm ratio (N:C), along with cell classification, localization, and segmentation. HemaX is trained and evaluated on a novel dataset, LeukoX, comprising 467 blood smear images encompassing ten (10) WBC types. The proposed model achieves impressive results, with an average classification accuracy of 81.08% and a Jaccard index of 89.16% for cell localization. Additionally, HemaX performs well in generating the five explanations with a normalized mean square error of 0.0317 for N:C ratio and over 80% accuracy for the other four attributes. Comprehensive experiments comparing against multiple state-of-the-art models demonstrate that HemaX's classification accuracy remains unaffected by its ability to provide explanations. Moreover, empirical analyses and validation by expert hematologists confirm the faithfulness of explanations predicted by our proposed model.

Identification of Small Objects in Satellite Image Benchmarks

Recent increases in aerial image access and volume, increases in computational power, and interest in applications have opened the door to scaling up object detection and domain adaptation research to production. Aerial data sets are very large in size, and each frame of the data set contains a huge number of dense and small objects. Deep learning applications for aerial imagery are behind due to a lack of training data, and researchers have recently turned to domain adaptation (DA) from a labeled data set to an unlabeled data set to alleviate the issue. These factors create two major challenges: the high variety between datasets (e.g. object sizes, class distributions, object feature uniformity, image acquisition, distance, weather conditions), and the size of objects in satellite imagery and subsequent failure of state-of-the-art to capture small objects, local features, and region proposals for densely overlapped objects in satellite image. In this paper, we propose two solutions to these problems: a domain discriminator to better align the local feature space between domains; and a novel pipeline that improves the back-end by spatial pyramid pooling, cross-stage partial network, region proposal network via heatmap-based region proposals, and object localization and identification through a novel image difficulty score that adapts the overall focal loss measure based on the image difficulty. Our proposed model outperformed the state-of-the-art method by 7.4%.