Background Bone marrow cytology is required to make a hematological diagnosis, influencing critical clinical decision points in hematology. However, bone marrow cytology is tedious, limited to experienced reference centers and associated with inter-observer variability. This may lead to a delayed or incorrect diagnosis, leaving an unmet need for innovative supporting technologies. Methods We develop an end-to-end deep learning-based system for automated bone marrow cytology. Starting with a bone marrow aspirate digital whole slide image, our system rapidly and automatically detects suitable regions for cytology, and subsequently identifies and classifies all bone marrow cells in each region. This collective cytomorphological information is captured in a representation called Histogram of Cell Types (HCT) quantifying bone marrow cell class probability distribution and acting as a cytological patient fingerprint. Results Our system achieves high accuracy in region detection (0.97 accuracy and 0.99 ROC AUC), and cell detection and cell classification (0.75 mean average precision, 0.78 average F1-score, Log-average miss rate of 0.31). Conclusions HCT has potential to eventually support more efficient and accurate diagnosis in hematology, supporting AI-enabled computational pathology.
Chest radiography has become the modality of choice for diagnosing pneumonia. However, analyzing chest X-ray images may be tedious, time-consuming and requiring expert knowledge that might not be available in less-developed regions. therefore, computer-aided diagnosis systems are needed. Recently, many classification systems based on deep learning have been proposed. Despite their success, the high development cost for deep networks is still a hurdle for deployment. Deep transfer learning (or simply transfer learning) has the merit of reducing the development cost by borrowing architectures from trained models followed by slight fine-tuning of some layers. Nevertheless, whether deep transfer learning is effective over training from scratch in the medical setting remains a research question for many applications. In this work, we investigate the use of deep transfer learning to classify pneumonia among chest Xray images. Experimental results demonstrated that, with slight fine-tuning, deep transfer learning brings performance advantage over training from scratch. Three models, ResNet-50, Inception V3 and DensetNet121, were trained separately through transfer learning and from scratch. The former can achieve a 4.1% to 52.5% larger area under the curve (AUC) than those obtained by the latter, suggesting the effectiveness of deep transfer learning for classifying pneumonia in chest X-ray images.
Deep learning models applied to healthcare applications including digital pathology have been increasing their scope and importance in recent years. Many of these models have been trained on The Cancer Genome Atlas (TCGA) atlas of digital images, or use it as a validation source. This study shows that there are tissue source site (tss) specific patterns of TCGA images that could be used to identify contributing institutions without any explicit training. Furthermore, it was observed that a model trained for cancer subtype classification has discovered such tss specific patterns within digital slides to classify cancer types. Digital scanner configuration and noise, tissue stain variation and artifacts, and source site patient demographics are among factors that likely account for the observed bias. Therefore, researchers should be cautious of such bias when using histopathology datasets for developing and training deep networks.
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