Diagnosing B-cell acute lymphoblastic leukemia in 2 pediatric patients with recent SARS-CoV-2 infection.

COVID-19 infection is still a mystery in terms of its long-term effect on health and its consequences on hematological disorders. Prior studies including ours have shown the abnormal changes in hematopoietic cells in COVID-19 patients. In this article, we are presenting 2 cases of pediatric B-lymphoblastic leukemia (B-ALL) with a previous history of COVID-19 infection.

Learning fast and fine-grained detection of amyloid neuropathologies from coarse-grained expert labels.

Precise, scalable, and quantitative evaluation of whole slide images is crucial in neuropathology. We release a deep learning model for rapid object detection and precise information on the identification, locality, and counts of cored plaques and cerebral amyloid angiopathies (CAAs). We trained this object detector using a repurposed image-tile dataset without any human-drawn bounding boxes.

Leukoerythroblastosis in a Sickle Cell Patient With Pregnancy: An Interesting Peripheral Blood Smear Finding.

The global pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has shaken the entire world. The social, health and financial impacts of this pandemic are beyond words. We have learnt a lot about this new disease in a short period of time, but still a long road to go to fully determine its pathogenic effect.

Intracranial myeloid sarcoma presentation in distant acute myeloid leukemia remission.

Intracranial myeloid sarcoma (IMS) is a rare central nervous system manifestation of hematopoietic neoplasms of myeloid origin. We report the first case of IMS treatment with an isocitrate dehydrogenase-2 (IDH-2) inhibitor, Enasidenib, following surgical resection, whole-brain radiation, and consolidation Etoposide/Cytarabine therapy. A 42-year-old female was diagnosed with IMS after a 10-year remission of her acute myeloid leukemia (AML).

Effects of Image Quantity and Image Source Variation on Machine Learning Histology Differential Diagnosis Models.

Aims: Histology, the microscopic study of normal tissues, is a crucial element of most medical curricula. Learning tools focused on histology are very important to learners who seek diagnostic competency within this important diagnostic arena. Recent developments in machine learning (ML) suggest that certain ML tools may be able to benefit this histology learning platform.

Limited Number of Cases May Yield Generalizable Models, a Proof of Concept in Deep Learning for Colon Histology.

Background: Little is known about the effect of a minimum number of slides required in generating image datasets used to build generalizable machine-learning (ML) models. In addition, the assumption within deep learning is that the increased number of training images will always enhance accuracy and that the initial validation accuracy of the models correlates well with their generalizability. In this pilot study, we have been able to test the above assumptions to gain a better understanding of such platforms, especially when data resources are limited.

Impact of pre-analytical variables on deep learning accuracy in histopathology.

Aims: Machine learning (ML) binary classification in diagnostic histopathology is an area of intense investigation. Several assumptions, including training image quality/format and the number of training images required, appear to be similar in many studies irrespective of the paucity of supporting evidence. We empirically compared training image file type, training set size, and two common convolutional neural networks (CNNs) using transfer learning (ResNet50 and SqueezeNet).