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 ...

In organelle imaging, segmentation aims to accurately delineate pixels or voxels corresponding to target organelles from background, noise, and other cellular structures in microscopy images, thereby ...

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 ...

Single-cell omics is a transformative area within life sciences, enabling researchers to untangle cellular heterogeneity, uncover developmental lineages, ...

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 ...

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.

Explore advances in protein imaging techniques and spatial proteomics. Learn how modern imaging tools reveal protein dynamics and organization in cells.

Researchers combined gigapixel microscopy with explainable AI to analyze pancreatic tissue from living donors. The system accurately classified type 2 diabetes and identified structural changes linked ...

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 ...