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1.
Inf inference ; 12(4): 2690-2719, 2023 Dec.
Article in English | MEDLINE | ID: mdl-37840543

ABSTRACT

Algorithmic stability is a concept from learning theory that expresses the degree to which changes to the input data (e.g. removal of a single data point) may affect the outputs of a regression algorithm. Knowing an algorithm's stability properties is often useful for many downstream applications-for example, stability is known to lead to desirable generalization properties and predictive inference guarantees. However, many modern algorithms currently used in practice are too complex for a theoretical analysis of their stability properties, and thus we can only attempt to establish these properties through an empirical exploration of the algorithm's behaviour on various datasets. In this work, we lay out a formal statistical framework for this kind of black-box testing without any assumptions on the algorithm or the data distribution, and establish fundamental bounds on the ability of any black-box test to identify algorithmic stability.

2.
PLoS One ; 16(10): e0258621, 2021.
Article in English | MEDLINE | ID: mdl-34710110

ABSTRACT

In patients with dense breasts or at high risk of breast cancer, dynamic contrast enhanced MRI (DCE-MRI) is a highly sensitive diagnostic tool. However, its specificity is highly variable and sometimes low; quantitative measurements of contrast uptake parameters may improve specificity and mitigate this issue. To improve diagnostic accuracy, data need to be captured at high spatial and temporal resolution. While many methods exist to accelerate MRI temporal resolution, not all are optimized to capture breast DCE-MRI dynamics. We propose a novel, flexible, and powerful framework for the reconstruction of highly-undersampled DCE-MRI data: enhancement-constrained acceleration (ECA). Enhancement-constrained acceleration uses an assumption of smooth enhancement at small time-scale to estimate points of smooth enhancement curves in small time intervals at each voxel. This method is tested in silico with physiologically realistic virtual phantoms, simulating state-of-the-art ultrafast acquisitions at 3.5s temporal resolution reconstructed at 0.25s temporal resolution (demo code available here). Virtual phantoms were developed from real patient data and parametrized in continuous time with arterial input function (AIF) models and lesion enhancement functions. Enhancement-constrained acceleration was compared to standard ultrafast reconstruction in estimating the bolus arrival time and initial slope of enhancement from reconstructed images. We found that the ECA method reconstructed images at 0.25s temporal resolution with no significant loss in image fidelity, a 4x reduction in the error of bolus arrival time estimation in lesions (p < 0.01) and 11x error reduction in blood vessels (p < 0.01). Our results suggest that ECA is a powerful and versatile tool for breast DCE-MRI.


Subject(s)
Algorithms , Breast Neoplasms/diagnosis , Breast/pathology , Contrast Media , Image Enhancement/methods , Magnetic Resonance Imaging/methods , Phantoms, Imaging , Computer Simulation , Female , Humans , Image Interpretation, Computer-Assisted
3.
ACS Appl Mater Interfaces ; 12(1): 1858-1866, 2020 Jan 08.
Article in English | MEDLINE | ID: mdl-31800201

ABSTRACT

Photovoltaic solar cells based on organic-inorganic hybrid halide perovskites have achieved a substantial breakthrough via advanced interface engineering. Reports have emphasized that combining the hybrid perovskites with the Lewis base and/or graphene can definitely improve the performance through surface trap passivation and band alignment alteration; the underlying mechanisms are not yet fully understood. Here, using density functional theory calculations, we show that upon the formation of CH3NH3PbI3 interfaces with three different Lewis base molecules and graphene, the binding strength with S-donors thiocarbamide and thioacetamide is higher than with O-donor dimethyl sulfoxide, while the interface dipole and work function reduction tend to increase from S-donors to O-donor. We provide evidences of deep trap state elimination in the S-donor perovskite interfaces through the analysis of defect formation on the CH3NH3PbI3(110) surface and of stability enhancement by estimation of activation barriers for vacancy-mediated iodine atom migrations. These theoretical predictions are in line with the experimental observation of performance enhancement in the perovskites prepared using thiocarbamide.

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