Embracing the Disharmony in Medical Imaging: A Simple and Effective Framework for Domain Adaptation

23 March 2021

Papers citing "Embracing the Disharmony in Medical Imaging: A Simple and Effective Framework for Domain Adaptation"

6 / 6 papers shown

Title
CEmb-SAM: Segment Anything Model with Condition Embedding for Joint Learning from Heterogeneous Datasets Dongik Shin Beomsu Kim Seungjun Baek MedIm 29 2 0 14 Aug 2023
Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation Brennan Nichyporuk Jillian Cardinell Justin Szeto Raghav Mehta J. Falet Douglas L. Arnold Sotirios A. Tsaftaris Tal Arbel 19 7 0 31 Oct 2022
Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience Rongguang Wang V. Bashyam Zhijian Yang Fanyang Yu Vasiliki Tassopoulou ... Dushyant Sahoo K. Nikita Ahmed Abdulkadir J. Wen Christos Davatzikos GAN MedIm AI4CE 28 46 0 14 Jun 2022
Bias in Machine Learning Models Can Be Significantly Mitigated by Careful Training: Evidence from Neuroimaging Studies Rongguang Wang Pratik Chaudhari Christos Davatzikos OOD AI4CE 21 43 0 26 May 2022
Domain Adaptation as a Problem of Inference on Graphical Models Anton van den Hengel Biwei Huang P. Stojanov Erdun Gao Qingsong Liu Clark Glymour OOD 43 64 0 09 Feb 2020
Bag of Tricks for Image Classification with Convolutional Neural Networks Tong He Zhi-Li Zhang Hang Zhang Zhongyue Zhang Junyuan Xie Mu Li 221 1,399 0 04 Dec 2018