Titlebook: Domain Adaptation and Representation Transfer; 4th MICCAI Workshop, Konstantinos Kamnitsas,Lisa Koch,Sotirios Tsaftari Conference proceedin

广口瓶

,Supervised Domain Adaptation Using Gradients Transfer for Improved Medical Image Analysis,d Domain Adaptation (SDA) strategies that focus on this challenge, assume the availability of a limited number of annotated samples from the new site. A typical SDA approach is to pre-train the model on the source site and then fine-tune on the target site. Current research has thus mainly focused o

公共汽车

陶醉

间谍活动

,Unsupervised Site Adaptation by Intra-site Variability Alignment,ferent target domain. This is known as the domain-shift problem. In this study, we propose a general method for transfer knowledge from a source site with labeled data to a target site where only unlabeled data is available. We leverage the variability that is often present within each site, the .,

摘要记录

碌碌之人

引导

,Feather-Light Fourier Domain Adaptation in Magnetic Resonance Imaging,en the sets are produced by different hardware. As a consequence of this ., a certain model might perform well on data from one clinic, and then fail when deployed in another. We propose a very light and transparent approach to perform .. The idea is to substitute the . low-frequency Fourier space c

易发怒

,Seamless Iterative Semi-supervised Correction of Imperfect Labels in Microscopy Images,growth of cells according to cytotoxicity grade under the microscope. Thus, human fatigue plays a role in error making, making the use of deep learning appealing. Due to the high cost of training data annotation, an approach without manual annotation is needed. We propose ., a new method for trainin

钝剑

,Task-Agnostic Continual Hippocampus Segmentation for Smooth Population Shifts,training and testing. We explore how such methods perform in a task-agnostic setting that more closely resembles dynamic clinical environments with gradual population shifts. We propose ODEx, a holistic solution that combines out-of-distribution detection with continual learning techniques. Validati

SHRIK

,Adaptive Optimization with Fewer Epochs Improves Across-Scanner Generalization of U-Net Based Medic are trained on images that have been acquired with a specific scanner, and are applied to images from another scanner. This indicates an overfitting to image characteristics that are irrelevant to the semantic contents, and is usually mitigated with data augmentation. We argue that early stopping a