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 ...
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.
Treatment modalities and survival outcomes in patients with hepatocellular carcinoma with brain metastases (HCC-BM): A large national study. This is an ASCO Meeting Abstract from the 2025 ASCO Annual ...
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 ...
In the rapidly evolving field of drug discovery, single-cell analysis has become an invaluable tool for understanding cellular heterogeneity and molecular pathways. However, traditional single-cell ...
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 ...
Researchers create a massive single-cell atlas of the aging mouse brain, revealing how epigenetic changes and "jumping genes" drive neurodegeneration.
Some results have been hidden because they may be inaccessible to you
Show inaccessible results