The first step to recovery is acceptance of this fact. Listen to more stories on the Noa app. This article was featured in the One Story to Read Today newsletter. "That's enough screen time for today," you tell your kid, urging them to turn off the video-game console or iPad. As for what they should do instead, you are not quite sure.

Multi-point tracking is a challenging task that involves detecting points in the scene and tracking them across a sequence of frames. Computing detection-based measures like the F-measure on a frame-by-frame basis is not sufficient to assess the overall performance, as it does not interpret performance in the temporal domain. The main evaluation metric available comes from Multi-object tracking (MOT) methods to benchmark performance on datasets such as KITTI with the recently proposed higher order tracking accuracy (HOTA) metric, which is capable of providing a better description of the performance over metrics such as MOTA, DetA, and IDF1. While the HOTA metric takes into account temporal associations, it does not provide a tailored means to analyse the spatial associations of a dataset in a multi-camera setup. Moreover, there are differences in evaluating the detection task for points when compared to objects (point distances vs. bounding box overlap). Therefore in this work, we propose a multi-view higher order tracking metric (mvHOTA) to determine the accuracy of multi-point (multi-instance and multi-class) tracking methods, while taking into account temporal and spatial associations.mvHOTA can be interpreted as the geometric mean of detection, temporal, and spatial associations, thereby providing equal weighting to each of the factors. We demonstrate the use of this metric to evaluate the tracking performance on an endoscopic point detection dataset from a previously organised surgical data science challenge. Furthermore, we compare with other adjusted MOT metrics for this use-case, discuss the properties of mvHOTA, and show how the proposed multi-view Association and the Occlusion index (OI) facilitate analysis of methods with respect to handling of occlusions. The code is available at https://github.com/Cardio-AI/mvhota.

Purpose: Mitral valve repair is a complex minimally invasive surgery of the heart valve. In this context, suture detection from endoscopic images is a highly relevant task that provides quantitative information to analyse suturing patterns, assess prosthetic configurations and produce augmented reality visualisations. Facial or anatomical landmark detection tasks typically contain a fixed number of landmarks, and use regression or fixed heatmap-based approaches to localize the landmarks. However in endoscopy, there are a varying number of sutures in every image, and the sutures may occur at any location in the annulus, as they are not semantically unique. Method: In this work, we formulate the suture detection task as a multi-instance deep heatmap regression problem, to identify entry and exit points of sutures. We extend our previous work, and introduce the novel use of a 2D Gaussian layer followed by a differentiable 2D spatial Soft-Argmax layer to function as a local non-maximum suppression. Results: We present extensive experiments with multiple heatmap distribution functions and two variants of the proposed model. In the intra-operative domain, Variant 1 showed a mean F1 of +0.0422 over the baseline. Similarly, in the simulator domain, Variant 1 showed a mean F1 of +0.0865 over the baseline. Conclusion: The proposed model shows an improvement over the baseline in the intra-operative and the simulator domains. The data is made publicly available within the scope of the MICCAI AdaptOR2021 Challenge https://adaptor2021.github.io/, and the code at https://github.com/Cardio-AI/suture-detection-pytorch/. DOI:10.1007/s11548-021-02523-w. The link to the open access article can be found here: https://link.springer.com/article/10.1007%2Fs11548-021-02523-w

It's Ada Lovelace Day and once again we're delighted to introduce you to "50 women in robotics you need to know about"! From the Afghanistan Girls Robotics Team to K.G.Engelhardt who in 1989 founded, and was the first Director of, the Center for Human Service Robotics at Carnegie Mellon, these women showcase a wide range of roles in robotics. We hope these short bios will provide a world of inspiration, in our ninth Women in Robotics list! They are researchers, industry leaders, and artists. Some women are at the start of their careers, while others have literally written the book, the program or the standards.

Urgent action is needed as it can take time to assess and address the serious risks this technology poses to human rights, warned the High Commissioner: "The higher the risk for human rights, the stricter the legal requirements for the use of AI technology should be". Ms. Bachelet also called for AI applications that cannot be used in compliance with international human rights law, to be banned. "Artificial intelligence can be a force for good, helping societies overcome some of the great challenges of our times. But AI technologies can have negative, even catastrophic, effects if they are used without sufficient regard to how they affect people's human rights". On Tuesday, the UN rights chief expressed concern about the « unprecedented level of surveillance across the globe by state and private actors », which she insisted was « incompatible » with human rights.

An unimaginable amount of data is continually being generated by scientific experiments, longitudinal studies, clinical trials, and hospital records--but what can be done with all this information? Barbara Engelhardt (she/her), PhD, is building machine-learning models and statistical tools to make use of that data and find ways to better understand, and even prevent, disease. She is now joining Gladstone Institutes as a senior investigator. "Barbara is an innovator in computational biology," says Katie Pollard, PhD, director of the Gladstone Institute of Data Science and Biotechnology. "She brings vast expertise in statistical models and will help expand our machine-learning program. We're thrilled she's joining our team."

A computational approach has the potential to help clinicians in intensive care units make better decisions about ordering common blood tests. Results of their study, presented earlier this month at the 2019 Pacific Symposium on Biocomputing, showed that using a machine learning algorithm developed by Princeton University researchers could have reduced the number of lab orders for white blood cell tests by as much as 44 percent. In addition, researchers demonstrated that their approach would have helped inform clinicians to intervene sometimes hours sooner when a patient's condition began to deteriorate. "With the lab test ordering policy that this method developed, we were able to order labs to determine that the patient's health had degraded enough to need treatment, on average, four hours before the clinician actually initiated treatment based on clinician ordered labs," says Barbara Engelhardt, senior author of the study and associate professor of computer science at Princeton. In their study, researchers leveraged the MIMIC III database--which includes detailed medical records of 58,000 critical care admissions at Boston's Beth Israel Deaconess Medical Center--and selected a subset of 6,060 records of adults who were admitted to the ICU between 2001 and 2012.

Doctors in intensive care units face a continual dilemma: Every blood test they order could yield critical information, but also adds costs and risks for patients. To address this challenge, researchers from Princeton University are developing a computational approach to help clinicians more effectively monitor patients' conditions and make decisions about the best opportunities to order lab tests for specific patients. Using data from more than 6,000 patients, graduate students Li-Fang Cheng and Niranjani Prasad worked with Associate Professor of Computer Science Barbara Engelhardt to design a system that could both reduce the frequency of tests and improve the timing of critical treatments. The team presented their results on Jan. 6 at the Pacific Symposium on Biocomputing in Hawaii. The analysis focused on four blood tests measuring lactate, creatinine, blood urea nitrogen and white blood cells.

"We don't have much ground truth in biology." According to Barbara Engelhardt, a computer scientist at Princeton University, that's just one of the many challenges that researchers face when trying to prime traditional machine-learning methods to analyze genomic data. Techniques in artificial intelligence and machine learning are dramatically altering the landscape of biological research, but Engelhardt doesn't think those "black box" approaches are enough to provide the insights necessary for understanding, diagnosing and treating disease. Instead, she's been developing new statistical tools that search for expected biological patterns to map out the genome's real but elusive "ground truth." Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.

"We don't have much ground truth in biology." According to Barbara Engelhardt, a computer scientist at Princeton University, that's just one of the many challenges that researchers face when trying to prime traditional machine-learning methods to analyze genomic data. Techniques in artificial intelligence and machine learning are dramatically altering the landscape of biological research, but Engelhardt doesn't think those "black box" approaches are enough to provide the insights necessary for understanding, diagnosing and treating disease. Instead, she's been developing new statistical tools that search for expected biological patterns to map out the genome's real but elusive "ground truth." Engelhardt likens the effort to detective work, as it involves combing through constellations of genetic variation, and even discarded data, for hidden gems.