@Radiology_AI 10 months ago
Differences in data distribution may affect federated #DeepLearning model performance in medical image segmentation https://doi.org/10.1148/ryai.220082 @ND_CSE @hku_science @ZJU_China #FederatedLearning #AI #MachineLearning #AIME23 #AIME2023 https://t.co/Sg0Y2jheGy
@Abhishek4273 3 years ago
Tried training an image segmentation model on PanNuke dataset, an Open Pan-Cancer Histology Dataset - https://github.com/Tessellate-Imaging/Monk_Object_Detection/tree/master/application_model_zoo (Ex 51) Data - https://jgamper.github.io/PanNukeDataset/ #100DaysOfCode #100DaysOfMLCode #301DaysOfCode #MachineLearning #Python #git #medical #Cancer #opensource #AI https://t.co/rFQ5JcvZjt
@PROCESS_H2020 3 years ago
This infographic provides an overal view of PROCESS´s use case 1 - "#Exascale Learning in Medical Image Data" 🧬 ✅ What is it about? ✅ What is it for? ✅ Why we do it? ✅ How we do it? ✅ What we will get? #HPC #MachineLearning Download it here https://www.process-project.eu/wp-content/uploads/2020/09/PROCESS_Infographic_UC1_Medical_Imaging.pdf https://t.co/99BDQ2JHMw