다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

The PASCAL Visual Object Classes Challenge

Theory of fuzzy integrals and its applications

Although monocular 3D human pose estimation methods have made significant progress, it's far from being solved due to the inherent depth ambiguity. Instead, exploiting multi-view information is a practical way to achieve absolute 3D human pose estimation. In this paper, we propose a simple yet effective pipeline for weakly-supervised cross-view 3D human pose estimation. By only using two camera views, our method can achieve state-of-the-art performance in a weakly-supervised manner, requiring no 3D ground truth but only 2D annotations. Specifically, our method contains two steps: triangulation and refinement. First, given the 2D keypoints that can be obtained through any classic 2D detection methods, triangulation is performed across two views to lift the 2D keypoints into coarse 3D poses.Then, a novel cross-view U-shaped graph convolutional network (CV-UGCN), which can explore the spatial configurations and cross-view correlations, is designed to refine the coarse 3D poses. In particular, the refinement progress is achieved through weakly-supervised learning, in which geometric and structure-aware consistency checks are performed. We evaluate our method on the standard benchmark dataset, Human3.6M. The Mean Per Joint Position Error on the benchmark dataset is 27.4 mm, which outperforms the state-of-the-arts remarkably (27.4 mm vs 30.2 mm).

Weakly-supervised Cross-view 3D Human Pose Estimation.

The study aims to develop a novel computer-aided diagnosis (CAD) scheme for mammographic breast mass classification using semi-supervised learning. Although supervised deep learning has achieved huge success across various medical image analysis tasks, its success relies on large amounts of high-quality annotations, which can be challenging to acquire in practice. To overcome this limitation, we propose employing a semi-supervised method, i.e., virtual adversarial training (VAT), to leverage and learn useful information underlying in unlabeled data for better classification of breast masses. Accordingly, our VAT-based models have two types of losses, namely supervised and virtual adversarial losses. The former loss acts as in supervised classification, while the latter loss aims at enhancing the model’s robustness against virtual adversarial perturbation, thus improving model generalizability. To evaluate the performance of our VAT-based CAD scheme, we retrospectively assembled a total of 1024 breast mass images, with equal number of benign and malignant masses. A large CNN and a small CNN were used in this investigation, and both were trained with and without the adversarial loss. When the labeled ratios were 40% and 80%, VAT-based CNNs delivered the highest classification accuracy of 0.740±0.015 and 0.760±0.015, respectively. The experimental results suggest that the VAT-based CAD scheme can effectively utilize meaningful knowledge from unlabeled data to better classify mammographic breast mass images.

Virtual adversarial training for semi-supervised breast mass classification

Although primates can facilely maintain long-duration tracking of an object without infection of occlusion or other near similar distracters, it remains a challenge for computer vision system. Studies in psychology suggest that the ability of primates to focus selective attention on the spatial properties of an object is necessary to observe object quickly and efficiently while focus selective attention on the feature properties of object is necessary to render it more prominent from the distracters. In this paper, we propose a novel spatial-feature attentional visual tracking (SFAVT) algorithm to encode both. In SFAVT, tracking is treated as an on-line binary classification problem where spatial attention is employed in early selective procedure to construct foreground/background appearance model by identifying image patches with good localization properties, and in late selective procedure to update models by maintaining image patches with good discrimitive motion properties. Meanwhile, feature attention works in mode seeking procedure to help select feature spaces that best separate a target from background. The on-line tuned adaptive appearance models by those selected feature spaces are used to train a classifier for target localization, then. Experiments under various real-world conditions show that this algorithm is able to track an object influenced by dramatic distracters while is of comparable time efficiency with meanshift.

Collaboration of spatial and feature attention for visual tracking

While β-hemolytic streptococcus (β-HS) infections are known to predispose patients to acute poststreptococcal glomerulonephritis, there is evidence that implicates α-hemolytic streptococcus (α-HS) in IgA nephropathy (IgAN). The alternative pathway of the complement system has also been implicated in IgAN. We aimed to explore the association between α-HS and complement activation in human tonsillar mononuclear cells (TMCs) in IgAN. In our study, α-HS induced higher IgA levels than IgG levels, while β-HS increased higher IgG levels than IgA levels with more activation-induced cytidine deaminase, in TMCs in the IgAN group. Aberrant IgA1 O-glycosylation levels were higher in IgAN patients with α-HS. C3 and C3b expression was decreased in IgAN patients, but in chronic tonsillitis control patients, the expression decreased only after stimulation with β-HS. Complement factor B and H (CFH) mRNA increased, but the CFH concentration in culture supernatants decreased with α-HS. The percentage of CD19 + CD35 + cells/complement receptor 1 (CR1) decreased with α-HS more than with β-HS, while CD19 + CD21 + cells/complement receptor 2 (CR2) increased more with β-HS than with α-HS. The component nephritis-associated plasmin receptor (NAPlr) of α-HS was not detected on tonsillar or kidney tissues in IgAN patients and was positive on cultured TMCs and mesangial cells. We concluded that α-HS induced the secretion of aberrantly O-glycosylated IgA while decreasing the levels of the inhibitory factor CFH in culture supernatants and CR1 + B cells. These findings provide testable mechanisms that relate α-HS infection to abnormal mucosal responses involving the alternative complement pathway in IgAN.

The influences of α-hemolytic Streptococcus on class switching and complement activation of human tonsillar cells in IgA nephropathy.

3D human mesh recovery from a 2D pose plays an important role in various applications. However, it is hard for existing methods to simultaneously capture the multiple relations during the evolution from skeleton to mesh, including joint-joint, joint-vertex and vertex-vertex relations, which often leads to implausible results. To address this issue, we propose a novel solution, called GATOR, that contains an encoder of Graph-Aware Transformer (GAT) and a decoder with Motion-Disentangled Regression (MDR) to explore these multiple relations. Specifically, GAT combines a GCN and a graph-aware self-attention in parallel to capture physical and hidden joint-joint relations. Furthermore, MDR models joint-vertex and vertex-vertex interactions to explore joint and vertex relations. Based on the clustering characteristics of vertex offset fields, MDR regresses the vertices by composing the predicted base motions. Extensive experiments show that GATOR achieves state-of-the-art performance on two challenging benchmarks.

Hong Liu

Papers

Weakly-supervised Cross-view 3D Human Pose Estimation.

Virtual adversarial training for semi-supervised breast mass classification

Collaboration of spatial and feature attention for visual tracking

The influences of α-hemolytic Streptococcus on class switching and complement activation of human tonsillar cells in IgA nephropathy.

GATOR: Graph-Aware Transformer with Motion-Disentangled Regression for Human Mesh Recovery from a 2D Pose