Developing a generalized segmentation model capable of simultaneously
de...
This paper presents a fully-automated method for the identification of
s...
We propose a spatio-temporal mixing kinematic data estimation method to
...
Recent studies have achieved outstanding success in explaining 2D image
...
CT image-based diagnosis of the stomach is developed as a new way of
dia...
This paper proposes a realistic image generation method for visualizatio...
We propose a depth estimation method from a single-shot monocular endosc...
This paper proposes a segmentation method of infection regions in the lu...
This paper proposes an automated segmentation method of infection and no...
Federated learning (FL) for medical image segmentation becomes more
chal...
This paper proposes a novel, unsupervised super-resolution (SR) approach...
The performance of deep learning-based methods strongly relies on the nu...
This paper proposes a fully automated atlas-based pancreas segmentation
...
This paper presents an automated classification method of infective and
...
This paper presents a colonoscope tracking method utilizing a colon shap...
This paper presents a super-resolution (SR) method with unpaired trainin...
In this work, we present a memory-efficient fully convolutional network ...
This paper presents a visualization method of intestine (the small and l...
This paper newly introduces multi-modality loss function for GAN-based
s...
Deep learning approaches based on convolutional neural networks (CNNs) h...
This paper presents a new approach for precisely estimating the renal
va...
This paper presents a fully automated atlas-based pancreas segmentation
...
This paper presents a colon deformation estimation method, which can be ...
Recent advances in deep learning, like 3D fully convolutional networks
(...
We propose a novel attention gate (AG) model for medical imaging that
au...
This paper presents a novel unsupervised segmentation method for 3D medi...
This paper presents a novel method for unsupervised segmentation of path...
One of the most common tasks in medical imaging is semantic segmentation...
Recent advances in 3D fully convolutional networks (FCN) have made it
fe...
Deep learning-based methods achieved impressive results for the segmenta...
Pancreas segmentation in computed tomography imaging has been historical...
Automatic multi-organ segmentation of the dual energy computed tomograph...
This paper presents an end-to-end pixelwise fully automated segmentation...
Computational anatomy allows the quantitative analysis of organs in medi...