To address these issues, we propose to adopt a full volume framework, which feeds the full volume brain image into the segmentation network and directly outputs the segmentation result for the whole brain volume.
Fine-grained classification of microscopic image data with limited samples is an open problem in computer vision and biomedical imaging.
In this paper, we propose an asymmetric occlusion-aware feature matching module, which can learn a rough occlusion mask that filters useless (occluded) areas immediately after feature warping without any explicit supervision.
Ranked #1 on Optical Flow Estimation on KITTI 2015
We present recursive cascaded networks, a general architecture that enables learning deep cascades, for deformable image registration.
3D medical image registration is of great clinical importance.
no code implementations • 22 Jul 2018 • Mitko Veta, Yujing J. Heng, Nikolas Stathonikos, Babak Ehteshami Bejnordi, Francisco Beca, Thomas Wollmann, Karl Rohr, Manan A. Shah, Dayong Wang, Mikael Rousson, Martin Hedlund, David Tellez, Francesco Ciompi, Erwan Zerhouni, David Lanyi, Matheus Viana, Vassili Kovalev, Vitali Liauchuk, Hady Ahmady Phoulady, Talha Qaiser, Simon Graham, Nasir Rajpoot, Erik Sjöblom, Jesper Molin, Kyunghyun Paeng, Sangheum Hwang, Sunggyun Park, Zhipeng Jia, Eric I-Chao Chang, Yan Xu, Andrew H. Beck, Paul J. van Diest, Josien P. W. Pluim
The best performing automatic method for the first task achieved a quadratic-weighted Cohen's kappa score of $\kappa$ = 0. 567, 95% CI [0. 464, 0. 671] between the predicted scores and the ground truth.
Based on our proposed framework, we first propose a method for cross-modality registration by fusing the deformation fields to adopt the cross-modality information from translated modalities.
The visual attributes of cells, such as the nuclear morphology and chromatin openness, are critical for histopathology image analysis.
The contributions of our algorithm are threefold: (1) We transplant traditional image registration algorithms to an end-to-end convolutional neural network framework, while maintaining the unsupervised nature of image registration problems.
The feature learning framework is designed to extract low- and mid-level features.
(2) We develop a deep week supervision formulation to exploit multi-scale learning under weak supervision within fully convolutional networks.
This paper proposes a multi-level feature learning framework for human action recognition using a single body-worn inertial sensor.
Methods: We leverage the idea of image-to-image prediction in recent deep learning by designing an algorithm that automatically exploits and fuses complex multichannel information - regional, location, and boundary cues - in gland histology images.
In this paper, we propose an innovative end-to-end subtitle detection and recognition system for videos in East Asian languages.
Here we leverage the idea of image-to-image prediction in recent deep learning by building a framework that automatically exploits and fuses complex multichannel information, regional, location and boundary patterns in gland histology images.
In this paper, we propose a new image instance segmentation method that segments individual glands (instances) in colon histology images.