Fluorescence-guided surgical system using holographic display: From phantom studies to canine patients

SIGNIFICANCE: Holographic display technology is a promising area of research that can lead to significant advancements in cancer surgery. We present the benefits of combining bioinspired multispectral imaging technology with holographic goggles for fluorescence-guided cancer surgery. Through a series of experiments with 43D-printed phantoms, small animal models of cancer, and surgeries on canine patients with head and neck cancer, we showcase the advantages of this holistic approach. AIM: The aim of our study is to demonstrate the feasibility and potential benefits of utilizing holographic display for fluorescence-guided surgery through a series of experiments involving 3D-printed phantoms and canine patients with head and neck cancer. APPROACH: We explore the integration of a bioinspired camera with a mixed reality headset to project fluorescent images as holograms onto a see-through display, and we demonstrate the potential benefits of this technology through benchtop and RESULTS: Our complete imaging and holographic display system showcased improved delineation of fluorescent targets in phantoms compared with the 2D monitor display approach and easy integration into the veterinarian surgical workflow. CONCLUSIONS: Based on our findings, it is evident that our comprehensive approach, which combines a bioinspired multispectral imaging sensor with holographic goggles, holds promise in enhancing the presentation of fluorescent information to surgeons during intraoperative scenarios while minimizing disruptions

Privacy Risks of Securing Machine Learning Models against Adversarial Examples

The arms race between attacks and defenses for machine learning models has come to a forefront in recent years, in both the security community and the privacy community. However, one big limitation of previous research is that the security domain and the privacy domain have typically been considered separately. It is thus unclear whether the defense methods in one domain will have any unexpected impact on the other domain. In this paper, we take a step towards resolving this limitation by combining the two domains. In particular, we measure the success of membership inference attacks against six state-of-the-art defense methods that mitigate the risk of adversarial examples (i.e., evasion attacks). Membership inference attacks determine whether or not an individual data record has been part of a model's training set. The accuracy of such attacks reflects the information leakage of training algorithms about individual members of the training set. Adversarial defense methods against adversarial examples influence the model's decision boundaries such that model predictions remain unchanged for a small area around each input. However, this objective is optimized on training data. Thus, individual data records in the training set have a significant influence on robust models. This makes the models more vulnerable to inference attacks. To perform the membership inference attacks, we leverage the existing inference methods that exploit model predictions. We also propose two new inference methods that exploit structural properties of robust models on adversarially perturbed data. Our experimental evaluation demonstrates that compared with the natural training (undefended) approach, adversarial defense methods can indeed increase the target model's risk against membership inference attacks.Comment: ACM CCS 2019, code is available at https://github.com/inspire-group/privacy-vs-robustnes

The effect of the oil resin on the properties of solution of the petroleum wax treated in an ultrasonic field

It was found that the complex treatment of ultrasonic followed by the addition of 0.3% by weight. petroleum resins, a more efficient method of inhibiting sedimentation processes than just ultrasonic or addition of 0,3% by weight. petroleum resins. According to the obtained data, fragments of aliphatic petroleum resins are adsorbed on the high molecular hydrocarbons of normal structure and prevent their aggregation thus the inhibition of sedimentation occurs

Current Multiple Myeloma Treatment Strategies with Novel Agents: A European Perspective

This review presents an overview of the most recent data using the novel agents thalidomide, bortezomib, and lenalidomide in the treatment of multiple myeloma and summarizes European treatment practices incorporating these novel agents

The role of visual experience in the emergence of cross-modal correspondences

Cross-modal correspondences describe the widespread tendency for attributes in one sensory modality to be consistently matched to those in another modality. For example, high pitched sounds tend to be matched to spiky shapes, small sizes, and high elevations. However, the extent to which these correspondences depend on sensory experience (e.g. regularities in the perceived environment) remains controversial. Two recent studies involving blind participants have argued that visual experience is necessary for the emergence of correspondences, wherein such correspondences were present (although attenuated) in late blind individuals but absent in the early blind. Here, using a similar approach and a large sample of early and late blind participants (N=59) and sighted controls (N=63), we challenge this view. Examining five auditory-tactile correspondences, we show that only one requires visual experience to emerge (pitch-shape), two are independent of visual experience (pitch-size, pitch-weight), and two appear to emerge in response to blindness (pitch-texture, pitch-softness). These effects tended to be more pronounced in the early blind than late blind group, and the duration of vision loss among the late blind did not mediate the strength of these correspondences. Our results suggest that altered sensory input can affect cross-modal correspondences in a more complex manner than previously thought and cannot solely be explained by a reduction in visually-mediated environmental correlations. We propose roles of visual calibration, neuroplasticity and structurally-innate associations in accounting for our findings

Obesity and Sugar‐sweetened Beverages in African‐American Preschool Children: A Longitudinal Study

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/93655/1/oby.2008.656.pd

Scalable and accurate deep learning for electronic health records

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record. We propose a representation of patients' entire, raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two U.S. academic medical centers with 216,221 adult patients hospitalized for at least 24 hours. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting in-hospital mortality (AUROC across sites 0.93-0.94), 30-day unplanned readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed state-of-the-art traditional predictive models in all cases. We also present a case-study of a neural-network attribution system, which illustrates how clinicians can gain some transparency into the predictions. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios, complete with explanations that directly highlight evidence in the patient's chart.Comment: Published version from https://www.nature.com/articles/s41746-018-0029-

Human Rights and German Intellectual History in Transnational Perspective

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156446/1/gequ12147.pd