Search Results for author:
Found 3 papers, 0 papers with code
Industrial and Medical Anomaly Detection Through Cycle-Consistent Adversarial Networks
The model would be able to identify its weaknesses by better learning how to transform an abnormal (respectively normal) image into a normal (respectively abnormal) one, helping the entire model to learn better than a single normal to normal reconstruction.
Composite Score for Anomaly Detection in Imbalanced Real-World Industrial Dataset
Nevertheless, several challenges have to be faced, including imbalanced datasets, the image complexity, and the zero-false-negative (ZFN) constraint to guarantee the high-quality requirement.
Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: A multi-institutional validation study
In a retrospective study encompassing DCE-MRI data from a total of 157 HER2+ breast cancer patients from 5 institutions, we developed and validated a deep learning approach for predicting pathological complete response (pCR) to HER2-targeted NAC prior to treatment.
