Deep learning for diagnosis and prognosis Lung adenocarcinoma is the most common type of lung cancer and one of the most lethal. Pathologists classify and grade histopathology slides from lobectomy ...

For the retrospective study, an ensemble 3D U-Net deep learning model was trained for lung tumor detection and segmentation using 1,504 CT scans with 1,828 segmented lung tumors.

Researchers have developed a deep learning model that, in certain conditions, is more than 71 percent accurate in predicting survival expectancy of lung cancer patients, significantly better than ...

A new deep learning model shows promise in detecting and segmenting lung tumors. The findings of the study could have important implications for lung cancer treatment.

The deep learning model, known as Sybil, predicts future lung cancer risk based on a single low-dose chest CT and can identify individuals who would benefit from regular monitoring.

A new machine learning model can classify lung cancer slides at the pathologist level Date: March 4, 2019 Source: Dartmouth-Hitchcock Medical Center Summary: Researchers have developed a deep ...

This study developed and validated a novel deep learning radiomic biomarker to estimate response to immune checkpoint inhibitor (ICI) therapy in advanced non–small cell lung cancer (NSCLC) using ...

Doctors and healthcare workers may one day use a machine learning model, called deep learning, to guide their treatment decisions for lung cancer patients, according to a team of Penn State Great ...