Recent years have witnessed great advances in applying deep learning to improve fluorescence microscopy imaging. However, enhancing the fidelity of image restoration networks and improving their ...

A machine learning model developed by researchers at the Johns Hopkins Kimmel Cancer Center filters out the biological noise ...

A machine learning model developed by researchers at the Johns Hopkins Kimmel Cancer Center filters out the biological noise in liquid biopsy samples, helping clinicians better match therapies to ...

Penn Engineers have developed an open-source algorithm that combines the speed of AI with the precision of geometry to ...

FireANTS combines AI and geometry to match features in dense images faster and more accurately, with potential applications in fields like radiology. Penn Engineers have developed an open-source ...

This repository was used to develop Mirai, the risk model described in: Towards Robust Mammography-Based Models for Breast Cancer Risk. Mirai was designed to predict risk at multiple time points, ...

The MinCrop version provides three methodicaly selected DCE-MRI time points (pre-contrast, early post-contrast, late post-contrast) cropped to 256×256 pixels around the main tumor. This version has ...

Abstract: Lung Cancer Incidence across the globe is the second leading cancer type tallying to about 2,206,771 during 2020 and is estimated to rise to about 3,503,378 by 2040 for both male and female ...

Abstract: One of the main causes of cancer-related deaths is lung cancer, and increasing survival rates requires early detection. The use of sophisticated machine learning (ML) algorithms to improve ...

This study aimed to evaluate the effectiveness of deep-learning models using transrectal ultrasound (TRUS) video clips in predicting prostate cancer. We manually segmented TRUS video clips from ...