This paper presents a neighborhood graph-induced pairwise constrained embedding framework under an orthogonal trace ratio (TR) criterion for manifold learning. In this embedding framework, the pairwise Cannot-Link and Must-Link constraints are applied to specify whether similarity pairs are in the same class or different classes. This setting makes the proposed framework flexible in regulating ...

the wisdom of crowds, an innovative theory described in social science, claims that the aggregate decisions made by a group will often be better than those of its individual members if the four fundamental criteria of this theory are satisfied. this theory used for in clustering problems. previous researches showed that this theory can significantly increase the stability and performance of lea...

Surgical tool detection in minimally invasive surgery is an essential part of computer-assisted interventions. Current approaches are mostly based on supervised methods requiring large annotated datasets. However, labelled datasets often scarce. Semi-supervised learning (SSL) has recently emerged as a viable alternative showing promise producing models retaining competitive performance to metho...

Image reconstruction from undersampled k-space data plays an important role in accelerating the acquisition of MR data, and a lot deep learning-based methods have been exploited recently. Despite achieved inspiring results, optimization these commonly relies on fully-sampled reference which are time-consuming difficult to collect. To address this issue, we propose novel self-supervised learning...

Fully-supervised salient object detection (SOD) methods have made great progress, but such often rely on a large number of pixel-level annotations, which are time-consuming and labour-intensive. In this paper, we focus new weakly-supervised SOD task under hybrid labels, where the supervision labels include coarse generated by traditional unsupervised method small real labels. To address issues ...

One of the main problems with biomedical signals is limited amount patient-specific data and significant time needed to record sufficient number samples for diagnostic treatment purposes. In this study, we present a framework simultaneously generate classify series based on modified Adversarial Autoencoder (AAE) algorithm one-dimensional convolutions. Our work breathing series, specific motivat...