Stacked Penalized Logistic Regression for Selecting Views in Multi-View Learning

In biomedical research, many different types of patient data can be collected, such as various types of omics data and medical imaging modalities. Applying multi-view learning to these different sources of information can increase the accuracy of medical classification models compared with single-view procedures. However, collecting biomedical data can be expensive and/or burdening for patients, so that it is important to reduce the amount of required data collection. It is therefore necessary to develop multi-view learning methods which can accurately identify those views that are most important for prediction. In recent years, several biomedical studies have used an approach known as multi-view stacking (MVS), where a model is trained on each view separately and the resulting predictions are combined through stacking. In these studies, MVS has been shown to increase classification accuracy. However, the MVS framework can also be used for selecting a subset of important views. To study the view selection potential of MVS, we develop a special case called stacked penalized logistic regression (StaPLR). Compared with existing view-selection methods, StaPLR can make use of faster optimization algorithms and is easily parallelized. We show that nonnegativity constraints on the parameters of the function which combines the views play an important role in preventing unimportant views from entering the model. We investigate the performance of StaPLR through simulations, and consider two real data examples. We compare the performance of StaPLR with an existing view selection method called the group lasso and observe that, in terms of view selection, StaPLR is often more conservative and has a consistently lower false positive rate.Comment: 26 pages, 9 figures. Accepted manuscrip

Semi-supervised Deep Multi-view Stereo

Significant progress has been witnessed in learning-based Multi-view Stereo (MVS) under supervised and unsupervised settings. To combine their respective merits in accuracy and completeness, meantime reducing the demand for expensive labeled data, this paper explores the problem of learning-based MVS in a semi-supervised setting that only a tiny part of the MVS data is attached with dense depth ground truth. However, due to huge variation of scenarios and flexible settings in views, it may break the basic assumption in classic semi-supervised learning, that unlabeled data and labeled data share the same label space and data distribution, named as semi-supervised distribution-gap ambiguity in the MVS problem. To handle these issues, we propose a novel semi-supervised distribution-augmented MVS framework, namely SDA-MVS. For the simple case that the basic assumption works in MVS data, consistency regularization encourages the model predictions to be consistent between original sample and randomly augmented sample. For further troublesome case that the basic assumption is conflicted in MVS data, we propose a novel style consistency loss to alleviate the negative effect caused by the distribution gap. The visual style of unlabeled sample is transferred to labeled sample to shrink the gap, and the model prediction of generated sample is further supervised with the label in original labeled sample. The experimental results in semi-supervised settings of multiple MVS datasets show the superior performance of the proposed method. With the same settings in backbone network, our proposed SDA-MVS outperforms its fully-supervised and unsupervised baselines.Comment: This paper is accepted in ACMMM-2023. The code is released at: https://github.com/ToughStoneX/Semi-MV

Influence of magnetic vestibular stimulation on self-motion perception

Ultrahigh magnetic fields (UHF) induce dizziness, vertigo and nystagmus due to Lorentz forces acting on the cupula in the semi-circular canals, an effect called magnetic vestibular stimulation (MVS) (Roberts et al., 2011; Ward et al., 2015). As the effect of the magnetic field on the cupula remains constant throughout the exposure, MVS is specifically suitable for studying cognitive performance under vestibular stimulation. The effect of MVS can be set near to zero by tilting the head 30° forward towards the body, allowing to compare different strengths of MVS within subjects (Mian et al., 2016). Furthermore, MVS serves as a suitable non-invasive model for unilateral failure of the vestibular system, which enables studying compensatory processes (Ertl and Boegle, 2019). We conducted our study in a Siemens Terra 7 Tesla Scanner and tested 8 young, healthy participants and plan to include 30 more. The study had two main goals. First, to investigate the process of perception-reflex uncoupling, as under MVS self-motion perception differs from measured nystagmus in direction as well as time course. While horizontal nystagmus was predominant, most participants report a percept of roll rotation, and less frequent a percept of yaw rotation or a mixture of both when moving in to and out of the magnetic field. This matches previous studies (Mian et al., 2013). Reported percepts did not correspond fully to measured reflexive eye-movements. Overall, stronger nystagmus indicated stronger percepts. Roll percepts make sense because the brain integrates the prior knowledge and sensory evidence. In supine position, yaw but not roll rotation would also elicit change in direction of gravity. Second, to quantify influence of continuous vestibular stimulation on cognitive functions with spatial components. Behavioral and neuroimaging studies have shown repeatedly that caloric, galvanic and motion platform-induced vestibular stimulation can affect performance in spatial tasks, such as mental rotation (Klaus et al., 2019; Falconer & Mast, 2012). The influence of MVS on spatial cognition is relevant for fMRI studies as MVS can be a confounder, especially in studies using UHFs. In our study, we did not find a meaningful effect of MVS on mental body rotation performance, neither in allocentric nor in egocentric strategy. In the future, we aim to compare healthy participants and patients with vestibular disorders to investigate adaption and habituation mechanisms

Deep Learning-based Prescription of Cardiac MRI Planes.

PurposeTo develop and evaluate a system to prescribe imaging planes for cardiac MRI based on deep learning (DL)-based localization of key anatomic landmarks.Materials and methodsAnnotated landmarks on 892 long-axis (LAX) and 493 short-axis (SAX) cine steady-state free precession series from cardiac MR images were retrospectively collected between February 2012 and June 2017. U-Net-based heatmap regression was used for localization of cardiac landmarks, which were used to compute cardiac MRI planes. Performance was evaluated by comparing localization distances and plane angle differences between DL predictions and ground truth. The plane angulations from DL were compared with those prescribed by the technologist at the original time of acquisition. Data were split into 80% for training and 20% for testing, and results confirmed with fivefold cross-validation.ResultsOn LAX images, DL localized the apex within mean 12.56 mm ± 19.11 (standard deviation) and the mitral valve (MV) within 7.68 mm ± 6.91. On SAX images, DL localized the aortic valve within 5.78 mm ± 5.68, MV within 5.90 mm ± 5.24, pulmonary valve within 6.55 mm ± 6.39, and tricuspid valve within 6.39 mm ± 5.89. On the basis of these localizations, average angle bias and mean error of DL-predicted imaging planes relative to ground truth annotations were as follows: SAX, -1.27° ± 6.81 and 4.93° ± 4.86; four chambers, 0.38° ± 6.45 and 5.16° ± 3.80; three chambers, 0.13° ± 12.70 and 9.02° ± 8.83; and two chamber, 0.25° ± 9.08 and 6.53° ± 6.28, respectively.ConclusionDL-based anatomic localization is a feasible strategy for planning cardiac MRI planes. This approach can produce imaging planes comparable to those defined by ground truth landmarks.© RSNA, 2019 Supplemental material is available for this article