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Evaluation associated with scientific specifics as predictive markers inside the growth and continuing development of intestinal tract cancers.
Average initial errors in the range of 22 to 38 mm were reduced to the range of 4 to 14 mm, corresponding to an error correction in the range of 63 to 85%. To our knowledge, this is the first markerless lung deformation compensation method dedicated to VATS and validated on actual clinical data.It has been a key topic to decompose the brain's spatial/temporal function networks from 4D functional magnetic resonance imaging (fMRI) data. With the advantages of robust and meaningful brain pattern extraction, deep neural networks have been shown to be more powerful and flexible in fMRI data modeling than other traditional methods. However, the challenge of designing neural network architecture for high-dimensional and complex fMRI data has also been realized recently. In this paper, we propose a new spatial/temporal differentiable neural architecture search algorithm (ST-DARTS) for optimal brain network decomposition. The core idea of ST-DARTS is to optimize the inner cell structure of the vanilla recurrent neural network (RNN) in order to effectively decompose spatial/temporal brain function networks from fMRI data. Based on the evaluations on all seven fMRI tasks in human connectome project (HCP) dataset, the ST-DARTS model is shown to perform promisingly, both spatially (i.e., it can recognize the most stimuli-correlated spatial brain network activation that is very similar to the benchmark) and temporally (i.e., its temporal activity is highly positively correlated with the task-design). To further improve the efficiency of ST-DARTS model, we introduce a flexible early-stopping mechanism, named as ST-DARTS+, which further improves experimental results significantly. To our best knowledge, the proposed ST-DARTS and ST-DARTS+ models are among the early efforts in optimally decomposing spatial/temporal brain function networks from fMRI data with neural architecture search strategy and they demonstrate great promise.Although deep learning models like CNNs have achieved great success in medical image analysis, the small size of medical datasets remains a major bottleneck in this area. To address this problem, researchers have started looking for external information beyond current available medical datasets. Traditional approaches generally leverage the information from natural images via transfer learning. More recent works utilize the domain knowledge from medical doctors, to create networks that resemble how medical doctors are trained, mimic their diagnostic patterns, or focus on the features or areas they pay particular attention to. In this survey, we summarize the current progress on integrating medical domain knowledge into deep learning models for various tasks, such as disease diagnosis, lesion, organ and abnormality detection, lesion and organ segmentation. For each task, we systematically categorize different kinds of medical domain knowledge that have been utilized and their corresponding integrating methods. We also provide current challenges and directions for future research.Fully annotated data sets play important roles in medical image segmentation and evaluation. Expense and imprecision are the two main issues in generating ground truth (GT) segmentations. 2-Deoxy-D-glucose modulator In this paper, in an attempt to overcome these two issues jointly, we propose a method, named SparseGT, which exploit variability among human segmenters to maximally save manual workload in GT generation for evaluating actual segmentations by algorithms. Pseudo ground truth (p-GT) segmentations are created by only a small fraction of workload and with human-level perfection/imperfection, and they can be used in practice as a substitute for fully manual GT in evaluating segmentation algorithms at the same precision. p-GT segmentations are generated by first selecting slices sparsely, where manual contouring is conducted only on these sparse slices, and subsequently filling segmentations on other slices automatically. By creating p-GT with different levels of sparseness, we determine the largest workload reduction achievable fs its advantage for objects with irregular shape change from slice to slice. An interpolation strategy for filling segmentations can achieve ∼60-90% of workload reduction in simulating human-level GT without the need of an actual training stage and shows potential in enlarging data sets for training p-GT generation networks. We conclude that not only over 90% reduction in workload is feasible without sacrificing evaluation accuracy but also the suitable strategy and the optimal sparseness level achievable for creating p-GT are object- and application-specific.The study aims to monitor the post-establishment success of the invasive skeleton shrimp Caprella scaura in the Atlantic-Mediterranean transition zone and understand its connectivity with other world areas, providing new information on the status of the introduced population and its global distribution. By using mitochondrial markers (16S and COI) we examined the temporal variation of populations in Cadiz Bay, Spain (hotspot for introductions in Europe) in between 2010 and 2017; as well as their linkage with foreign populations in its native and introduced distribution ranges. Cadiz Bay populations exhibited a connection with several European introduced populations (Iberian Peninsula, Canary Islands, Mediterranean Sea and The Netherlands), eastern USA, Sea of Japan and Australia. We found no evidence to support a Brazilian origin (one potential native area) of the Iberian Peninsula populations. We identified a progressive decrease in haplotype diversity and a low connectivity at the end of the monitoring period in one of the stations. Human-mediated changes in propagule pressure, and unfavorable environmental fluctuations are probably responsible for this. Meanwhile, populations in Cadiz Bay count on numerous foreign donors that could easily refuel the propagule input by exchanging gene flow. This implies that a vector regulation strategy has the potential of compromising the success of established non-native populations, which usually undergo vulnerability periods due to the challenging conditions of marinas. The use of molecular tools in a time series approach is then useful to identify the ideal time window to put in action management measures so that they are cost-effective.
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