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DenseSeg: joint learning for semantic segmentation and landmark detection using dense image-to-shape representation.
Int J Comput Assist Radiol Surg
January 2025
Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany.
Purpose: Semantic segmentation and landmark detection are fundamental tasks of medical image processing, facilitating further analysis of anatomical objects. Although deep learning-based pixel-wise classification has set a new-state-of-the-art for segmentation, it falls short in landmark detection, a strength of shape-based approaches.
Methods: In this work, we propose a dense image-to-shape representation that enables the joint learning of landmarks and semantic segmentation by employing a fully convolutional architecture.
Comparative genomic prediction of resistance to Fusarium wilt (Fusarium oxysporum f. sp. niveum race 2) in watermelon: parametric and nonparametric approaches.
Theor Appl Genet
January 2025
USDA, ARS, U.S. Vegetable Laboratory, 2700 Savannah Highway, Charleston, SC, 29414, USA.
Complex traits influenced by multiple genes pose challenges for marker-assisted selection (MAS) in breeding. Genomic selection (GS) is a promising strategy for achieving higher genetic gains in quantitative traits by stacking favorable alleles into elite cultivars. Resistance to Fusarium oxysporum f.
View Article and Find Full Text PDFPrognostic and clinical heterogeneity of PD1 and PD-L1- immunohistochemical scores in endometrial cancers.
Arch Gynecol Obstet
January 2025
Department of Gynecology and Obstetrics, University Medical Center Schleswig-Holstein, Campus-Lübeck, Lübeck, Germany.
Introduction: PD1/PD-L1 inhibition (ICi) has recently become a new standard of care for patients with advanced MMR-deficient (MMRd) endometrial cancers. Nevertheless, response to immunotherapy is more complex than the presence of a single biomarker and therefore it remains challenging to predict patients response to ICi beyond MMRd tumors. Elevated PD-L1 expression (CPS ≥ 1) is often used as a prognostic marker as well as a predictive biomarker of response to ICi in different tumor types.
View Article and Find Full Text PDFMultilayer entropy-weighted TOPSIS method and its decision-making in ecological operation during the subsidence period of the Three Gorges Reservoir.
Sci Rep
January 2025
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, 430,072, China.
Coordinating the downstream ecological demand and the power generation demand of hydropower stations is an important task in the operation of reservoirs, and how to evaluate the ecological satisfaction of the scheduling process is a difficult problem that needs to be solved urgently. A multi-objective optimal reservoir scheduling model was constructed to coordinate the spawning flow demand of " Four Major Chinese Carps"; The model takes the maximum power generation and the maximum membership degree of downstream river ecological water demand as the objective functions, and uses the dynamic programming multi-objective solution algorithm based on penalty factors to solve the problem, and obtains the non-inferior solution set in each scenario. The multilayer entropy-weighted TOPSIS method was used to study the non-inferior solution of the multi-objective scheduling model of the Three Gorges Reservoir, and the satisfactory solution ranking of the river flow rise process, ecological flow-related requirements, and power generation water requirements was obtained under the four schemes including 4d ~ 7d, which improved the reliability of the evaluation results and made up for the shortcomings of the traditional TOPSIS method in terms of hierarchy and weight science.
View Article and Find Full Text PDFComparing clinical only and combined clinical laboratory models for ECPR outcomes in refractory cardiac arrest.
Sci Rep
January 2025
Department Emergency and Critical Care Medicine, Changhua Christian Hospital, Changhua, 50006, Taiwan.
Extracorporeal cardiopulmonary resuscitation (ECPR) improves survival for prolonged cardiac arrest (CA) but carries significant risks and costs due to ECMO. Previous predictive models have been complex, incorporating both clinical data and parameters obtained after CPR or ECMO initiation. This study aims to compare a simpler clinical-only model with a model that includes both clinical and pre-ECMO laboratory parameters, to refine patient selection and improve ECPR outcomes.
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