MSCDA: Multi-level semantic-guided contrast improves unsupervised domain adaptation for breast MRI segmentation in small datasets.

Deep learning (DL) applied to breast tissue segmentation in magnetic resonance imaging (MRI) has received increased attention in the last decade, however, the domain shift which arises from different vendors, acquisition protocols, and biological heterogeneity, remains an important but challenging obstacle on the path towards clinical implementation. In this paper, we propose a novel Multi-level Semantic-guided Contrastive Domain Adaptation (MSCDA) framework to address this issue in an unsupervised manner. Our approach incorporates self-training with contrastive learning to align feature representations between domains.

The effects of the interaction of genetic predisposition with lifestyle factors on bladder cancer risk.

Objectives: To investigate the association of polygenic risk score (PRS) and bladder cancer (BC) risk and whether this PRS can be offset by a healthy lifestyle.

Methods: Individuals with BC (n = 563) and non-BC controls (n = 483 957) were identified in the UK Biobank, and adjusted Cox regression models were used. A PRS was constructed based on 34 genetic variants associated with BC development, while a healthy lifestyle score (HLS) was constructed based on three lifestyle factors (i.

Broad-UNet: Multi-scale feature learning for nowcasting tasks.

Weather nowcasting consists of predicting meteorological components in the short term at high spatial resolutions. Due to its influence in many human activities, accurate nowcasting has recently gained plenty of attention. In this paper, we treat the nowcasting problem as an image-to-image translation problem using satellite imagery.

Vegetable intake and the risk of bladder cancer in the BLadder Cancer Epidemiology and Nutritional Determinants (BLEND) international study.

Background: Although a potential inverse association between vegetable intake and bladder cancer risk has been reported, epidemiological evidence is inconsistent. This research aimed to elucidate the association between vegetable intake and bladder cancer risk by conducting a pooled analysis of data from prospective cohort studies.

Methods: Vegetable intake in relation to bladder cancer risk was examined by pooling individual-level data from 13 cohort studies, comprising 3203 cases among a total of 555,685 participants.

Grain and dietary fiber intake and bladder cancer risk: a pooled analysis of prospective cohort studies.

Background: Higher intakes of whole grains and dietary fiber have been associated with lower risk of insulin resistance, hyperinsulinemia, and inflammation, which are known predisposing factors for cancer.

Objectives: Because the evidence of association with bladder cancer (BC) is limited, we aimed to assess associations with BC risk for intakes of whole grains, refined grains, and dietary fiber.

Methods: We pooled individual data from 574,726 participants in 13 cohort studies, 3214 of whom developed incident BC.

Deep neural-kernel blocks.

This paper introduces novel deep architectures using the hybrid neural-kernel core model as the first building block. The proposed models follow a combination of a neural networks based architecture and a kernel based model enriched with pooling layers. In particular, in this context three kernel blocks with average, maxout and convolutional pooling layers are introduced and examined.

Regularized Semipaired Kernel CCA for Domain Adaptation.

Domain adaptation learning is one of the fundamental research topics in pattern recognition and machine learning. This paper introduces a regularized semipaired kernel canonical correlation analysis formulation for learning a latent space for the domain adaptation problem. The optimization problem is formulated in the primal-dual least squares support vector machine setting where side information can be readily incorporated through regularization terms.

Robust Support Vector Machines for Classification with Nonconvex and Smooth Losses.

This letter addresses the robustness problem when learning a large margin classifier in the presence of label noise. In our study, we achieve this purpose by proposing robustified large margin support vector machines. The robustness of the proposed robust support vector classifiers (RSVC), which is interpreted from a weighted viewpoint in this work, is due to the use of nonconvex classification losses.

Incremental multi-class semi-supervised clustering regularized by Kalman filtering.

This paper introduces an on-line semi-supervised learning algorithm formulated as a regularized kernel spectral clustering (KSC) approach. We consider the case where new data arrive sequentially but only a small fraction of it is labeled. The available labeled data act as prototypes and help to improve the performance of the algorithm to estimate the labels of the unlabeled data points.

Multiclass semisupervised learning based upon kernel spectral clustering.

This paper proposes a multiclass semisupervised learning algorithm by using kernel spectral clustering (KSC) as a core model. A regularized KSC is formulated to estimate the class memberships of data points in a semisupervised setting using the one-versus-all strategy while both labeled and unlabeled data points are present in the learning process. The propagation of the labels to a large amount of unlabeled data points is achieved by adding the regularization terms to the cost function of the KSC formulation.

Approximate solutions to ordinary differential equations using least squares support vector machines.

In this paper, a new approach based on least squares support vector machines (LS-SVMs) is proposed for solving linear and nonlinear ordinary differential equations (ODEs). The approximate solution is presented in closed form by means of LS-SVMs, whose parameters are adjusted to minimize an appropriate error function. For the linear and nonlinear cases, these parameters are obtained by solving a system of linear and nonlinear equations, respectively.