[HTML][HTML] Learning disentangled representations in the imaging domain
Disentangled representation learning has been proposed as an approach to learning
general representations even in the absence of, or with limited, supervision. A good general …
general representations even in the absence of, or with limited, supervision. A good general …
Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
Deep learning has demonstrated remarkable performance across various tasks in medical
imaging. However, these approaches primarily focus on supervised learning, assuming that …
imaging. However, these approaches primarily focus on supervised learning, assuming that …
Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing
Automatic liver tumor segmentation would have a big impact on liver therapy planning
procedures and follow-up assessment, thanks to standardization and incorporation of full …
procedures and follow-up assessment, thanks to standardization and incorporation of full …
Unsupervised domain adaptation via disentangled representations: Application to cross-modality liver segmentation
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 …
generally performs poorly on data from different target domains due to domain shifts …
A survey on deep learning and explainability for automatic report generation from medical images
Every year physicians face an increasing demand of image-based diagnosis from patients, a
problem that can be addressed with recent artificial intelligence methods. In this context, we …
problem that can be addressed with recent artificial intelligence methods. In this context, we …
Unsupervised wasserstein distance guided domain adaptation for 3d multi-domain liver segmentation
Deep neural networks have shown exceptional learning capability and generalizability in
the source domain when massive labeled data is provided. However, the well-trained …
the source domain when massive labeled data is provided. However, the well-trained …
Real-time biomechanical modeling of the liver using machine learning models trained on finite element method simulations
OJ Pellicer-Valero, MJ Rupérez… - Expert Systems with …, 2020 - Elsevier
The development of accurate real-time models of the biomechanical behavior of different
organs and tissues still poses a challenge in the field of biomechanical engineering. In the …
organs and tissues still poses a challenge in the field of biomechanical engineering. In the …
Toward reliable automatic liver and tumor segmentation using convolutional neural network based on 2.5 D models
G Wardhana, H Naghibi, B Sirmacek… - International journal of …, 2021 - Springer
Purpose We investigated the parameter configuration in the automatic liver and tumor
segmentation using a convolutional neural network based on 2.5 D model. The …
segmentation using a convolutional neural network based on 2.5 D model. The …
Review of Disentanglement Approaches for Medical Applications--Towards Solving the Gordian Knot of Generative Models in Healthcare
J Fragemann, L Ardizzone, J Egger… - arxiv preprint arxiv …, 2022 - arxiv.org
Deep neural networks are commonly used for medical purposes such as image generation,
segmentation, or classification. Besides this, they are often criticized as black boxes as their …
segmentation, or classification. Besides this, they are often criticized as black boxes as their …
AC-E network: attentive context-enhanced network for liver segmentation
Segmentation of liver from CT scans is essential in computer-aided liver disease diagnosis
and treatment. However, the 2DCNN ignores the 3D context, and the 3DCNN suffers from …
and treatment. However, the 2DCNN ignores the 3D context, and the 3DCNN suffers from …