63 research outputs found
Collective phenomena in quasi-two-dimensional fermionic polar molecules: band renormalization and excitons
We theoretically analyze a quasi-two-dimensional system of fermionic polar
molecules in a harmonic transverse confining potential. The renormalized energy
bands are calculated by solving the Hartree-Fock equation numerically for
various trap and dipolar interaction strengths. The inter-subband excitations
of the system are studied in the conserving time-dependent Hartree-Fock (TDHF)
approximation from the perspective of lattice modulation spectroscopy
experiments. We find that the excitation spectrum consists of both
inter-subband particle-hole excitation continuums and anti-bound excitons,
arising from the anisotropic nature of dipolar interactions. The excitonic
modes capture the majority of the spectral weight. We also evaluate the
inter-subband transition rates in order to investigate the nature of the
excitonic modes and find that they are anti-bound states formed from
particle-hole excitations arising from several subbands. Our results indicate
that the excitonic effects are present for interaction strengths and
temperatures accessible in current experiments with polar molecules.Comment: 21 pages, 12 figure
Competition between pairing and ferromagnetic instabilities in ultracold Fermi gases near Feshbach resonances
We study the quench dynamics of a two-component ultracold Fermi gas from the
weak into the strong interaction regime, where the short time dynamics are
governed by the exponential growth rate of unstable collective modes. We obtain
an effective interaction that takes into account both Pauli blocking and the
energy dependence of the scattering amplitude near a Feshbach resonance. Using
this interaction we analyze the competing instabilities towards Stoner
ferromagnetism and pairing.Comment: 4+epsilon pages, 4 figure
Universal behavior of repulsive two-dimensional fermions in the vicinity of the quantum freezing point
We show by a meta-analysis of the available Quantum Monte Carlo (QMC) results that two-dimensional fermions with repulsive interactions exhibit universal behavior in the strongly correlated regime, and that their freezing transition can be described using a quantum generalization of the classical Hansen-Verlet freezing criterion. We calculate the liquid-state energy and the freezing point of the 2D dipolar Fermi gas (2DDFG) using a variational method by taking ground-state wave functions of 2D electron gas (2DEG) as trial states. A comparison with the recent fixed-node diffusion Monte Carlo analysis of the 2DDFG shows that our simple variational technique captures more than 95% of the correlation energy, and predicts the freezing transition within the uncertainty bounds of QMC. Finally, we utilize the ground-state wave functions of 2DDFG as trial states and provide a variational account of the effects of finite 2D confinement width. Our results indicate significant beyond mean-field effects. We calculate the frequency of collective monopole oscillations of the quasi-2D dipolar gas as an experimental demonstration of correlation effects.Physic
Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation
Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis
and treatment. However, variations in MRI acquisition protocols result in
different appearances of normal and diseased tissue in the images.
Convolutional neural networks (CNNs), which have shown to be successful in many
medical image analysis tasks, are typically sensitive to the variations in
imaging protocols. Therefore, in many cases, networks trained on data acquired
with one MRI protocol, do not perform satisfactorily on data acquired with
different protocols. This limits the use of models trained with large annotated
legacy datasets on a new dataset with a different domain which is often a
recurring situation in clinical settings. In this study, we aim to answer the
following central questions regarding domain adaptation in medical image
analysis: Given a fitted legacy model, 1) How much data from the new domain is
required for a decent adaptation of the original network?; and, 2) What portion
of the pre-trained model parameters should be retrained given a certain number
of the new domain training samples? To address these questions, we conducted
extensive experiments in white matter hyperintensity segmentation task. We
trained a CNN on legacy MR images of brain and evaluated the performance of the
domain-adapted network on the same task with images from a different domain. We
then compared the performance of the model to the surrogate scenarios where
either the same trained network is used or a new network is trained from
scratch on the new dataset.The domain-adapted network tuned only by two
training examples achieved a Dice score of 0.63 substantially outperforming a
similar network trained on the same set of examples from scratch.Comment: 8 pages, 3 figure
Artificial intelligence in cancer imaging: Clinical challenges and applications
Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care
Transcriptional and Cellular Diversity of the Human Heart
Background: The human heart requires a complex ensemble of specialized cell types to perform its essential function. A greater knowledge of the intricate cellular milieu of the heart is critical to increase our understanding of cardiac homeostasis and pathology. As recent advances in low-input RNA sequencing have allowed definitions of cellular transcriptomes at single-cell resolution at scale, we have applied these approaches to assess the cellular and transcriptional diversity of the nonfailing human heart. Methods: Microfluidic encapsulation and barcoding was used to perform single nuclear RNA sequencing with samples from 7 human donors, selected for their absence of overt cardiac disease. Individual nuclear transcriptomes were then clustered based on transcriptional profiles of highly variable genes. These clusters were used as the basis for between-chamber and between-sex differential gene expression analyses and intersection with genetic and pharmacologic data. Results: We sequenced the transcriptomes of 287 269 single cardiac nuclei, revealing 9 major cell types and 20 subclusters of cell types within the human heart. Cellular subclasses include 2 distinct groups of resident macrophages, 4 endothelial subtypes, and 2 fibroblast subsets. Comparisons of cellular transcriptomes by cardiac chamber or sex reveal diversity not only in cardiomyocyte transcriptional programs but also in subtypes involved in extracellular matrix remodeling and vascularization. Using genetic association data, we identified strong enrichment for the role of cell subtypes in cardiac traits and diseases. Intersection of our data set with genes on cardiac clinical testing panels and the druggable genome reveals striking patterns of cellular specificity. Conclusions: Using large-scale single nuclei RNA sequencing, we defined the transcriptional and cellular diversity in the normal human heart. Our identification of discrete cell subtypes and differentially expressed genes within the heart will ultimately facilitate the development of new therapeutics for cardiovascular diseases
Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound
Lung ultrasound (LUS) is an important imaging modality used by emergency
physicians to assess pulmonary congestion at the patient bedside. B-line
artifacts in LUS videos are key findings associated with pulmonary congestion.
Not only can the interpretation of LUS be challenging for novice operators, but
visual quantification of B-lines remains subject to observer variability. In
this work, we investigate the strengths and weaknesses of multiple deep
learning approaches for automated B-line detection and localization in LUS
videos. We curate and publish, BEDLUS, a new ultrasound dataset comprising
1,419 videos from 113 patients with a total of 15,755 expert-annotated B-lines.
Based on this dataset, we present a benchmark of established deep learning
methods applied to the task of B-line detection. To pave the way for
interpretable quantification of B-lines, we propose a novel "single-point"
approach to B-line localization using only the point of origin. Our results
show that (a) the area under the receiver operating characteristic curve ranges
from 0.864 to 0.955 for the benchmarked detection methods, (b) within this
range, the best performance is achieved by models that leverage multiple
successive frames as input, and (c) the proposed single-point approach for
B-line localization reaches an F1-score of 0.65, performing on par with the
inter-observer agreement. The dataset and developed methods can facilitate
further biomedical research on automated interpretation of lung ultrasound with
the potential to expand the clinical utility.Comment: 10 pages, 4 figure
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. The automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their methods on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge. Sixty T1 + FLAIR images from three MR scanners were released with the manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. The segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: 1) Dice similarity coefficient; 2) modified Hausdorff distance (95th percentile); 3) absolute log-transformed volume difference; 4) sensitivity for detecting individual lesions; and 5) F1-score for individual lesions. In addition, the methods were ranked on their inter-scanner robustness; 20 participants submitted their methods for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all the methods generalize to unseen scanners. The challenge remains open for future submissions and provides a public platform for method evaluation
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