8 citations · 11 across the 3 of their papers we have counts for
7 papers
Unsupervised Wasserstein Distance Guided Domain Adaptation for 3D Multi-Domain Liver Segmentation
Chenyu You, Junlin Yang, Julius Chapiro +1
Deep neural networks have shown exceptional learning capability and generalizability in the source domain when massive labeled data is provided. However, the well-trained models of…
2018 Robotic Scene Segmentation Challenge
Max Allan, Satoshi Kondo, Sebastian Bodenstedt +38
In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward ki…
Hepatocellular Carcinoma Intra-arterial Treatment Response Prediction for Improved Therapeutic Decision-Making
Junlin Yang, Nicha C. Dvornek, Fan Zhang +4
This work proposes a pipeline to predict treatment response to intra-arterial therapy of patients with Hepatocellular Carcinoma (HCC) for improved therapeutic decision-making. Our…
Decision Explanation and Feature Importance for Invertible Networks
Juntang Zhuang, Nicha C. Dvornek, Xiaoxiao Li +2
Deep neural networks are vulnerable to adversarial attacks and hard to interpret because of their black-box nature. The recently proposed invertible network is able to accurately r…
Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation
Junlin Yang, Nicha C. Dvornek, Fan Zhang +3
A deep learning model trained on some labeled data from a certain source domain generally performs poorly on data from different target domains due to domain shifts. Unsupervised d…
Domain-Agnostic Learning with Anatomy-Consistent Embedding for Cross-Modality Liver Segmentation
Junlin Yang, Nicha C. Dvornek, Fan Zhang +4
Domain Adaptation (DA) has the potential to greatly help the generalization of deep learning models. However, the current literature usually assumes to transfer the knowledge from…