Today's computer-aided diagnosis (CAD) model is still far from the clinical practice of glaucoma detection, mainly due to the training bias originating from 1) the normal-abnormal class imbalance and 2) the rare but significant hard samples in fundus images.
Anomaly detection is essential for preventing hazardous outcomes for safety-critical applications like autonomous driving.
The nature of thick-slice scanning causes severe inter-slice discontinuities of 3D medical images, and the vanilla 2D/3D convolutional neural networks (CNNs) fail to represent sparse inter-slice information and dense intra-slice information in a balanced way, leading to severe underfitting to inter-slice features (for vanilla 2D CNNs) and overfitting to noise from long-range slices (for vanilla 3D CNNs).
Accurately detecting and tracking multi-objects is important for safety-critical applications such as autonomous navigation.
In this study, we propose a united adversarial learning framework (UAL) for simultaneous liver tumors segmentation and detection using multi-modality NCMRI.
Dialogue summarization has been extensively studied and applied, where the prior works mainly focused on exploring superior model structures to align the input dialogue and the output summary.
We propose a novel weakly supervised learning framework, Cycle Prototype Network, for 3D renal compartment segmentation.
We introduce a system for optimal resource allocation that can predict performance with aggressive trade-offs, for both new and past observed queries.
We focus on the value of collecting information at current time, and on that of collecting sequential information, we illustrate how these values are related and we discuss how IA and IOV can occur in those settings.
3)We propose the adversarial weighted ensemble module which uses the trained discriminators to evaluate the quality of segmented structures, and normalizes these evaluation scores for the ensemble weights directed at the input image, thus enhancing the ensemble results.
Our results indicate that both bulk phase and isolated $VS_4$ NWs are semiconductors with band gaps of 2. 24 and 2. 64 eV, respectively, and that they prefer the antiferromagnetic (AFM) ground state based on DFT calculations.
Materials Science Strongly Correlated Electrons Chemical Physics Computational Physics Quantum Physics A.0
The Cobb angle that quantitatively evaluates the spinal curvature plays an important role in the scoliosis diagnosis and treatment.
Optical transient surveys have led to the discovery of dozens of stellar tidal disruption events (TDEs) by massive black hole in the centers of galaxies.
High Energy Astrophysical Phenomena Astrophysics of Galaxies
Quantum simulator with the ability to harness the dynamics of complex quantum systems has emerged as a promising platform for probing exotic topological phases.
Deep learning-based medical image registration and segmentation joint models utilize the complementarity (augmentation data or weakly supervised data from registration, region constraints from segmentation) to bring mutual improvement in complex scene and few-shot situation.
Tracking and grasping a dynamic object with a random trajectory is even harder.
In this paper, we propose the neural-symbolic learning (NSL) framework that performs human-like learning by unifying deep neural learning and symbolic logical reasoning for the spinal medical report generation.
no code implementations • 2 Feb 2020 • Guang Yang, Jun Chen, Zhifan Gao, Shuo Li, Hao Ni, Elsa Angelini, Tom Wong, Raad Mohiaddin, Eva Nyktari, Ricardo Wage, Lei Xu, Yanping Zhang, Xiuquan Du, Heye Zhang, David Firmin, Jennifer Keegan
Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently (~0. 27 seconds to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60-68 2D slices).
We build on the idea of model predictive shielding (MPS), where a backup controller is used to override the learned policy as needed to ensure safety.
Our method is built as an end-to-end framework for segmentation and classification.
The proposed DMQCA model consists of a multiview module with two attention mechanisms, a key-frame module, and a regression module, to achieve direct accurate multiple-index estimation.
Experiments on 1440 myocardium segments of 90 subjects from short axis MR sequences of multiple lengths prove that Cardiac-MOS achieves reliable performance, with correlation of 0. 926 for motion score index estimation and accuracy of 77. 4\% for motion scoring.
The CARN architecture is composed of a cascade amplifier network (CAN) for expressive feature embedding and a linear regression model for multiple indices estimation.
And cGAN advantageously fuses substantial 3D spatial context information from 3D echocardiography by self-learning structured loss; 2) For the first time, it embeds the atlas into an end-to-end optimization framework, which uses 3D LV atlas as a powerful prior knowledge to improve the inference speed, address the lower contrast and the limited annotation problems of 3D echocardiography; 3) It combines traditional discrimination loss and the new proposed consistent constraint, which further improves the generalization of the proposed framework.
Image-set classification has recently generated great popularity due to its widespread applications in computer vision.
Accurate detection of the myocardial infarction (MI) area is crucial for early diagnosis planning and follow-up management.
Cardiac left ventricle (LV) quantification is among the most clinically important tasks for identification and diagnosis of cardiac diseases, yet still a challenge due to the high variability of cardiac structure and the complexity of temporal dynamics.
With the uniform distribution of random points, our proposed method achieves more accurate results compared with other methods, which demonstrates the robustness and accuracy for the volume calculation of CT lung lesions.
Accurate estimation of regional wall thicknesses (RWT) of left ventricular (LV) myocardium from cardiac MR sequences is of significant importance for identification and diagnosis of cardiac disease.
However, estimation of multitype cardiac indices with consistently reliable and high accuracy is still a great challenge due to the high variability of cardiac structures and complexity of temporal dynamics in cardiac MR sequences.
Direct methods have recently emerged as an effective and efficient tool in automated medical image analysis and become a trend to solve diverse challenging tasks in clinical practise.
This is partially because our DTs overcome the extreme greediness of the MST.
In this paper, we propose a novel supervised descriptor learning (SDL) algorithm to establish a discriminative and compact feature representation for multi-output regression.
This paper describes the acquisition of a large scale and high quality parallel corpora for English and Chinese.