Overview of deep learning-based cell image analysis. A typical analysis pipeline consists of a retraining module and an inference module: the inference module directly produces estimated metrics.

Recent advancements in deep learning have transformed the analysis of blood cell images and the classification of leukemia. By employing complex neural network architectures, such as convolutional ...

A computational method called scSurv, developed by researchers at Institute of Science Tokyo, links individual cells to ...

Using Early Biomarker Change and Treatment Adherence to Predict Risk of Relapse Among Patients With Chronic Myeloid Leukemia Who Are in Remission The imaging cohort consisted of positron emission ...

Completed phase 1a dose escalation study of the first oral ENPP1 inhibitor RBS2418 immunotherapy in subjects with metastatic solid tumors. SECN-15: A novel treatment option for patients with ...

One of the most puzzling aspects of common chronic inflammatory skin diseases such as psoriasis is how they become chronic.

This figure shows how the STAIG framework can successfully identify spatial domains by integrating image processing and contrastive learning to analyze spatial transcriptomics data effectively.

A team of researchers has constructed the most detailed single-cell map of the adult human prostate to date, cataloging more ...

Metabolic-associated steatotic liver disease (MASLD) is a clinically heterogeneous condition with highly variable outcomes affecting more than 30% ...

During early development, tissues and organs begin to form through the shifting, splitting, and growing of many thousands of cells. A team of researchers headed by MIT engineers has now developed a ...

Sepsis is globally recognized as a primary contributor to morbidity and mortality. The Sepsis 3.0 definition is captured as ...