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Real time Augmented Reality Registration of Stretchable Structures for Needle Navigation through the Gluteus Muscles

Research

Feb 24th, 2022
Real time Augmented Reality Registration of Stretchable Structures for Needle

Researchers: Dr. Dan Raviv, (Electrical Engineering), Shai Tejman-Yarden, M.D., Edmond and Lily Safra Children's Hospital, Chaim Sheba Medical Center & Sackler Faculty of Medicine

  • Vision
  • Health-Biomedicine
  • AI for social good

Virtual Reality (VR) and Augmented Reality (AR) are two popular platforms to enhance human vision by providing additional layers of information not visible to the naked eye. The gaming industry is blooming using this technology, and the tech giants have recently manufactured high accuracy VR/AR glasses.

 

In this proposal, we claim that we can use the same hardware but add improved AI algorithms of alignment between stretchable domains and project pre-scanned CT images on top of the soft tissues. The current alignment done in a virtual scenario is not sufficient for surgical procedures, especially where there is a movement of the skin and fat tissues between the CT scans and the 3D positioning of the body during the medical procedure. While brain surgery navigation is accurate enough for computer assistance, it is built for hard tissues (bones) and requires heavy preparation per patient. Here we focus on a quick solution for soft tissues with minimal resource requirements.

 

Using state-of-the-art rigid and non-rigid geometric deep learning alignment algorithms developed recently in Dr. Raviv’s lab and the medical expertise from Dr. Tejman-Yarden’s lab, we believe we can solve a fundamental medical task. Using superior algorithms, we plan to provide a robust real-time data fusion tool of soft tissues for surgical needs. We plan to focus first on needle insertion through the Gluteus Muscles, but the same approach can be used for multiple organs and multiple treatments.

 

 

General Falsification Tests for Instrumental Variables

Research

Feb 24th, 2022
General Falsification Tests for Instrumental Variables

Researchers: Dr. Oren Danieli (Economics), Dr. Daniel Nevo (Statistics & Operations Research), Dr. Dan Zeltzer (Economics)

  • Fundamentals of AI and DS
  • Economy and Finance
  • AI for social good

Instrumental variable (IV) estimation is a widely used method that supports high stakes government policies and business decisions, when controlled experiments are not feasible or when a treatment is not successfully delivered to every unit in a randomized experiment.

 

In this research project, we will develop statistical methods for assessing the validity of such designs. IV designs rely on exclusion restriction assumptions that are not directly testable and that are therefore challenging to assess empirically. In this context, this project aims to formalize the logic of falsification tests, a set of tests that leverage contextual knowledge about the absence of causal links (for example, from future to past outcomes) to test the validity of candidate instruments.

 

We will establish that IV falsification tests can be mapped to a class of prediction problems that can leverage current machine learning methods. Based on this conceptualization, we will develop general methods that would both improve falsification test efficiency and help guide the construction of such tests.

 

These methods will be particularly applicable to research using large datasets with many candidate variables that can be used for falsification, an increasingly common situation for which no formal methods currently exist. Developing methods for evaluating and improving the validity of these common research designs entails clear societal benefits.

 

 

 

Photo: Leon Levy Dead Sea Scrolls Digital Library, Israel Antiquities Authority; color photographer Shai Halevi, infrared by Najib Anton Albina

Research

Feb 24th, 2022
Opening the Dead Sea Scrolls to the World

Researchers: Prof. Nachum Dershowitz (Computer Science), Prof. Jonathan Ben-Dov (Biblical Studies)

  • NLP
  • Digital Humanities
  • AI for social good

Our goal in this collaborative project is to adapt various algorithms in computer vision and machine learning (segmentation, registration, and alignment), turning them into practical methods that can be applied to the whole photographic collection of Dead Sea Scrolls (DSS), including even very fragmentary ones from Qumran.

 

The resulting tools are already active in the website of Scripta Qumranica Electronica (https://sqe.deadseascrolls.org.il), operated by the Israel Antiquities Authority (IAA), and are improved as the project advances. The algorithms thus greatly enhance the usability of the DSS collection, which enjoys enormous interest in the public sphere due to its overwhelming cultural and historical importance abd the open access granted by the IAA.

 

With such highly fragmentary scrolls, the registration and alignment of the rich photographic log is a significant asset for improving the reconstruction of scrolls and expose hitherto unknown texts. The website – augmented by our advanced algorithms - brings multiple images and texts together for the benefit of scholars and laypersons alike, as well as enables a new wave of scholarly editions of this highly difficult and fragmentary material.

 

The figure shows examples of recent color images on the left and an old IR image on the right. The two arrows indicate matches.

 

 

Early warning for invasive Mediterranean fishes

Research

Feb 24th, 2022
Early warning for invasive Mediterranean fishes

Researchers: Prof. Jonathan Belmaker (Zoology), Prof. Raja Giryes (Electrical Engineering)

  • Vision
  • Environment
  • AI for social good

The global marine environment is undergoing dramatic and rapid human induced changes. Alien and invasive species (AIS) are a major source of biodiversity loss and have caused major economic losses and threats to human health. While the magnitude of AIS in the Mediterranean is unprecedented, we lack simple and effective approaches to track AIS and understand resulting changes in ecological community structure and ecosystem dynamics. 

 

Here, we propose to use the wealth of information available in digital citizen science and social media repositories with a combination of automated image recognition and ecological modelling, to gain comprehensive insights on spatio-temporal dynamics of Mediterranean fish populations. By calibrating the tools in the Red Sea, the source of introduction to the Mediterranean, we will be able to detect new introductions at the initial stage, where densities are low and identification is difficult, providing a critical early warning for AIS.

 

 

Digital sources will be combined with a set of cameras along the Israeli coast to produce an automated tool that will continuously track changes, map results, and update range dynamic predictions. This will enable elucidating several key aspects of fish dynamics, such as undetected AIS introductions, incipient range expansion of AIS, migration routes, range size declines of Indigenous species, and changes in phenology associated with climate change.

 

This near real-time monitoring of fish dynamics will provide critical new information for scientists, policy makers, and the general public needed towards appropriate preparation for this emerging threat.

Prediction of Rain intensity classes from near-real-time geOsynchronous Satellites and PrECipitation imagery Time-series (PROSPECT). Photo by Marek Piwnicki on Unsplash

Research

Feb 3rd, 2022
Prediction of Rain intensity classes from near-real-time geOsynchronous

Researcher: Dr. Michal Segal-Rozenhaimer (Exact Sciences)

  • Vision
  • Environment
  • AI for social good

Extreme precipitation events are becoming more frequent and in areas where previously never existed. Accurate/prior prediction of light, medium and intense rain events is important on a time-scales of hours to allow adequate warnings and mitigation planning, especially in remote and developing countries. Indeed, such predictions are available (albeit not optimal yet) by numerical weather prediction (NWP) models or by ground-based radar systems.

 

However, NWP models are very costly in terms of processing time, memory, and expert-knowledge needs, while ground-based radars are not widespread enough, especially not in developing countries or in remote regions (e.g. desert areas or over the ocean). 

 

Here, we propose to utilize available near-real-time wide-coverage high temporal resolution geosynchronous satellite imagery and sparse precipitation data to construct a machine learning model for predicting rain intensity classes for short-term (0-6 hours) applications. We propose a tailor-made machine-learning architecture that combines both spatial (e.g. CNN) and time-series (ESN, LSTM) machine-learning approaches that will allow the ingestion and fusion of satellite-data on different times and spatial scales.

 

We are looking to apply our expertise in cloud detection and classification and retrieval approaches, with the support from the TAD center experts to derive a fast and simple yet useful enough rain predictor that can be used globally and can be easily embedded and implemented on a publicly available web-platform.

Research

Aug 11th, 2021
The phylogenetic tree reconstruction game: developing reinforcement-learning

Researchers: Tal Pupko (Life Sciences), Yishay Mansour (Computer Science), Itay Mayrose (Life Sciences) 

  • Health-Biomedicine
  • Fundamentals of AI and DS
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