Nine Veni grants for AI researchers

The Dutch Research Council (NWO) has awarded nine Veni grants to researchers involved in groundbreaking AI research. The recipients can use the grants – up to a maximum of 280,000 per researcher – to further develop their research ideas over the next three years. ICAI wants to congratulate these colleagues for their achievements!

The AI recipients of a Veni grant:

  • Continual Learning under Human Guidance
    dr. E.T. Nalisnick (M), Universiteit van Amsterdam
    Artificial intelligence (AI) systems need to adapt to new scenarios. Yet, we must ensure that the new behaviours and skills that they acquire are safe. The researcher will develop AI techniques that allow autonomous systems to adapt but to do so cautiously, under the guidance of a human.
  • Intelligent interactive natural language systems you can trust and control
    dr. V. Niculae (M), Universiteit van Amsterdam
    Artificial intelligence agents are seemingly approaching human performance in natural language tasks like automatic translation and dialogue. However, deployed in the wild, such systems are out of control, learning to produce harmful language even unprompted. Using recent machine learning breakthroughs, the researcher rethinks language generation for trustworthiness and controllability.
  • Efficient AI with material-based neural networks
    dr. H.C. Ruiz Euler (M), Universiteit Twente
    The unprecedented success of artificial intelligence (AI) comes at the price of unsustainable computational costs. This project will research the potential of a novel technology for highly efficient AI hardware: “material-based neural networks”. This technology will enable the next generation of efficient AI systems for edge computing and autonomous systems.
  • Helping computers say what they mean to say
    dr. J.D. Groschwitz (M), Universiteit van Amsterdam
    When a computer talks to us, for example when answering a question, it must translate that answer from its inner computer representation to fluent human language. This project combines linguistics and state-of-the-art machine learning to create a language generation system in which the output text expresses exactly what the computer meant to say.
  • The life and death of white dwarf binary stars
    dr. J.C.J. van Roestel (M), Universiteit van Amsterdam
    Double white dwarf stars are a rare but important type of binary star. They are potential supernova progenitors, some merge to form massive rotating white dwarfs, and they also emit gravitational wave radiation. I will combine data from the Dutch BlackGEM telescope with multiple other telescope surveys and use novel machine learning methods to uncover the population of short-period eclipsing white dwarf binary stars across the entire sky. By comparing the observed population and characteristics with binary population synthesis models, I will determine how these double white dwarfs end their life.
  • Personalizing radiotherapy with Artificial Intelligence: reducing the toxicity burden for cancer survivors
    Lisanne van Dijk PhD, University Medical Center Groningen (UMCG)
    Many head and neck cancer patients suffer from persistent severe toxicities following radiotherapy. As survival rates increase, toxicity reduction has become more pivotal. This project uses Artificial Intelligence techniques to predict toxicity trajectories, which can facilitate personalized decision-support to guide physicians in finding optimal strategies to reduce these severe toxicities.
  • Towards realistic models for spatiotemporal data
    dr. K. Kirchner (V), Technische Universiteit Delft
    Many environmental factors, such as temperature or air pollution, are recorded at several locations and dates. Because of limited computing power, a realistic analysis of the resulting large datasets is often unachievable. This project develops computational approaches which enable efficient accurate data analysis and reliable forecasts for phenomena with uncertainty.
  • Explainable Artificial Intelligence to unravel genetic architecture of complex traits
    Gennady Roshchupkin PhD, Erasmus MC Medical Center
    While we have learned that most diseases have a genetic component, we are still far away from understanding the underlying processes. Using Artificial Intelligence, I will investigate the complex relationship between DNA mutations and human health. This will be the basis for development of novel diagnostic, prognostic and therapeutic tools.
  • Neural networks for efficient storage and communication of information
    dr. J. Townsend (M), Universiteit van Amsterdam
    The brain is an extremely efficient system for storing and communicating information. This research will study the use of artificial neural networks, inspired by the mechanisms in the brain, for data compression, enabling faster internet communication and more efficient storage of computer files.

Find the complete list of Veni grants 2021 here.

Limburg welcomes first ICAI Lab for the development of AI for patient care

Knowledge institutions from Limburg will join forces with local parties in healthcare and IT. The first ICAI Lab in the region – the ICAI Brightlands Smart Health Lab – is committed to making breakthroughs in patient care by developing and deploying artificial intelligence.

ICAI Brightlands Smart Health Lab

With the arrival of the ICAI Brightlands Smart Health Lab, Limburg joins the national network of ICAI Labs. In this new lab, researchers from Maastricht University and Zuyd University of Applied Sciences will collaborate with healthcare providers, the Netherlands Comprehensive Cancer Organisation, medical IT service provider ilionx, and several Brightlands campuses and organisations.

The activities of the new lab span the entire process from the development to the deployment of artificial intelligence in healthcare. Among other topics, the lab focuses on preparing medical data so that it can be used anonymously for ‘training’ artificial intelligence. Other key activities of the ICAI Brightlands Smart Health Lab include the development of artificial intelligence and putting the technology into practice. The lab specifically focuses on artificial intelligence that can predict how a patient’s disease will develop. In this way, the technology can help map out treatment plans for diseases like cancer.

Ecosystem

Professor Andre Dekker, one of the lab’s academic directors alongside Dr. Rianne Fijten and Dr. Alberto Traverso, considers the collaboration between all parties as crucial for the success of their joint vision for the future. Dekker: “Bringing together knowledge and data from very diverse parties in the Brightlands ecosystem is precisely what allows us to conduct relevant research, develop real solutions and put these into practice.”

Responsibly connecting data

Dekker and his team bring their expertise in responsible sharing of privacy-sensitive medical data. The ICAI Brightlands Smart Health Lab thereby builds on their work on the Personal Health Train: a network that allows healthcare providers to learn from each other’s data, but in which data never leaves the host institution’s management and through which the privacy of patients is preserved.

Dr. Rianne Fijten: “Data is often an abstract concept for researchers, but it comes from real people. That is why careful handling of privacy-sensitive medical data is very important to us.”

Partners in the ICAI Brightlands Smart Health Lab

The ICAI Brightlands Smart Health Lab is a collaboration between Maastricht University, Zuyd University of Applied Sciences, Maastro Clinic, Maastricht UMC+, ilionx, Netherlands Comprehensive Cancer Organisation (IKNL), Brightlands Institute for Smart Society, Brightlands Smart Services Campus, Brightlands Maastricht Health Campus and AI Hub Brightlands.

Future Impact joins ICAI Launch Pad

Future Impact, a tech-job focused platform for young talent, and ICAI will collaborate in the Launch Pad programme. Launch Pad is initiated by ICAI to connect young AI talent to the Dutch ecosystem. Together with Future Impact and the Dutch AI Coalition, ICAI wants to further expand Launch Pad into a pro-active support programme for AI careers in the Netherlands.

ICAI believes that the Netherlands offers great opportunities in the field of AI, but that AI talent and the organizations that need this talent are not yet able to find each other. The aim of Launch Pad is to connect AI talent to the Dutch ecosystem by providing a matchmaking process between AI-PhD students and Dutch companies looking for AI talent. Future Impact will fulfill the role of matchmaker.

Partnership

All partners in the Launch Pad collaboration have a specific expertise and role in the matchmaking process. ICAI will use its network of ICAI labs to connect with PhD students who are about to enter the labor market. Future Impact will focus on the recruitment and matching of young AI talent. And the Dutch AI Coalition (NLAIC) will provide a network of organizations who are involved with AI in the Netherlands.

Awareness of AI opportunities

The Launch Pad programme wants to create awareness amongst students of the possibilities in the Dutch ecosystem. At the moment, many trained PhD students go (back) abroad, without being aware of the possibilities and favorable living conditions in the Netherlands. ICAI wants more students to stay in the Netherlands after pursuing their PhD.

Future Impact as matchmaker

Future Impact will act as matchmaker and introduce AI talent to Dutch based partners. The platform has a broad network of relevant organizations looking for AI talent. In addition to the matchmaking role, Future Impact offers support throughout the whole matchmaking and application process. This includes coaching candidates in finding their areas of interest, helping them to prepare for job interviews and negotiating contracts.

ICAI mission

One of the missions of ICAI is to create and nurture a national AI knowledge and talent ecosystem. ICAI Launch Pad is the next practical step in carrying out this mission.

Registration open now! ICAI Day: A Deep Dive into AI


On the 27th of October ICAI organizes the ICAI Day: A Deep Dive into AI. This hybrid event will take place on location in Den Bosch and online. The focus of this ICAI event will be on the technological side of AI. Registration for the event is now open for everybody who is interested. You can register here.

Together with the ELSA Labs community of NLAIC, we organize a lunch event as part of the ICAI day for all the labs. In small table settings we will deep dive into specific area’s and how to work on AI with trustworthiness integrated in the technology. Introductions will come from the Police Lab and ELSA Lab. You must be physically present to attend this part of the programme.

We have speakers from outside and inside the labs who will dive into the latest technologies of AI and show work on Geometric Deep Learning: from Euclid to drug design (Michael Bronstein, Imperial College & Twitter) and graph convolution networks (Xie Weiyi, Radboud MC/Thira Lab). Finally our experts in the labs, from academia and industry, will share insides on lessons learned from the collaborations in the ICAI labs.


The event will take place in two parts:

12:00 – 13:30 Part 1: Lunch table discussions
With introductions of:
Heleen Rutjes (TU Eindhoven, ELSA Labs)
Maurits Bleeker (UvA, Police Lab)
Sarah Ibrahimi (UvA, Police Lab)

Topics of the lunch table discussions:
– Inclusive society with data engineering
– Autonomous systems in mobility
– Robotics & autonomous agents
– Computer vision in healthcare
– Using AI in education and governmental org.
– Online personalization and impact

13:45 – 17:00 Part 2: ICAI plenary event
13:45 – 14:00 Welcome by chair Nathan de Groot and director of ICAI Maarten de Rijke
14:00 – 14:45 Keynote Michael Bronstein Geometric Deep Learning: from Euclid to drug design (Twitter, Imperial College)

14:45 – 15:00 Break

15:00 – 15:30 Lecture Xie Weiyi (Thira Lab) – Graph Attention Networks for airway labeling
15:30 – 15:35 Short videos of different ICAI labs
15:35 – 16:20 Discussion table ICAI labs –Lessons learned in collaboration:
Elvan Kula (ING, AI for Fintech Lab),
Georgios Tsatsaronis (Elsevier, Discovery Lab),
Cees Snoek (UvA, QUVA, AIM & Atlas Lab)
16:20 – 16:30 Closing words

16:30 – 17:30 Drinks


We look forward welcoming you at our event!

Register here

LTP Program ROBUST wins NWO support

ICAI is very proud to be able to expand the network of ICAI with the LTP ROBUST program “Trustworthy AI systems for sustainable growth”, supported by NWO in the new Long Term Program with €25 million.

AI technology promises to help with many tough societal challenges. For the technology to be adopted and benefit everyone, it is essential that the AI systems that we develop are trustworthy. The ROBUST Long-Term Program addresses this challenge and gets the opportunity to build the program.

First and foremost, ROBUST focuses on attracting talent to work on the challenges of trustworthy AI. Talent is the core of any AI ecosystem. Second, it makes trustworthy AI research and innovation a shared responsibility between knowledge institutes, industry, governmental organizations, and other societal stakeholders. And third, it practices learning by doing in the Dutch context, through use-inspired research, connections with startups and SMEs, and an extensive knowledge sharing efforts.

17 new ICAI labs

The ROBUST program builds on ICAI, the Innovation Center for Artificial Intelligence. It intends to add 17 labs to ICAI’s current ecosystem of 30 labs, in areas as diverse as health, energy, logistics, and services. The labs that make up ROBUST are driven by economic opportunities and contributions to the UN’s sustainable development goals. They will develop AI-algorithms that advance the state of the art in accuracy, reliability, repeatability, resilience, and safety of AI algorithms – all essential hallmarks of trustworthy AI.

ROBUST is a collaboration of 21 knowledge institutes, 23 companies, and 10 societal organizations. ROBUST is supported by the Netherlands Organisation for Scientific Research (NWO) and the AiNed National Growth Fund Investment Program.

Project team

The project leader for the ROBUST program is prof. Maarten de Rijke of the University of Amsterdam and ICAI. The co-applicants are prof. Mark van den Brand (Technical University Eindhoven), prof. Arie van Deursen (Delft University of Technology), prof. Bram van Ginneken (RadboudUMC), dr. Eva van Rikxoort (Thirona), prof. Clarisa Sánchez Gutiérrez (University of Amsterdam), and prof. Nava Tintarev (Maastricht University).

Contact: Esther Smit, esmit@icai.ai

Read the NWO press release here.

LUMO Labs makes first TTT.AI pre-seed investment in LUMC innovation partner Autoscriber

LUMO Labs announces a TTT.AI investment in Autoscriber, a Dutch health tech software startup developed in a clinical setting. Autoscriber is developing AI-supported voice recognition software to capture and summarize healthcare professional-patient consultations. TTT.AI is part of the ICAI Venture Program.

Autoscriber’s value proposition is at the intersection of three crucial healthcare trends:

  • affordable and accessible healthcare for all
  • growing importance of structured/discrete data capture to support data-driven healthcare initiatives
  • increasing desire for understanding and self-determination among patients

Health professionals access Autoscriber as a subscription based software-as-a-service (SaaS) solution to large hospitals and practices, smaller practices and General Practitioners. LUMO Labs’ pre-seed funding will allow Autoscriber to go live in multiple hospitals during the next 12 months.

The technology, which promises to streamline clinical interactions into a seamless experience for patients and caregivers, was developed in collaboration with the Clinical Artificial Intelligence and Research Lab (CAIRELab) at Leiden University Medical Center.

“We are very excited to work so closely with the LUMC. Every design choice we make we validate with the physician in a clinical setting.” said Koen Bonenkamp, co-founder and CTO.

Autoscriber software records, transcribes and extracts clinical concepts during consultations. It allows for automated summaries and integration in the patient’s Electronic Health Record that can be easily edited by the physician, providing real-time support for diagnostics and personalized care.

LUMO Labs is investing because Autoscriber meets and/or exceeds their investment fundamentals, including a strong entrepreneurial founding team with profound expertise, proof-of-concept and the promise to dramatically improve the lives of caregivers and patients.

“The problem Autoscriber is solving is universal: reducing time and money spent on repetitive, administrative tasks by physicians while increasing transparency, comprehensibility and human interaction in deeply personal treatment situations,” said LUMO Labs founding partner Andy Lürling. “Their solution is dynamic and highly scalable because of the strong technological and human-centered set-up.”

Netherlands Cancer Institute, Elekta and UvA launch POP-AART Lab

The Partnership for Online Personalized AI-driven Adaptive Radiation Therapy (POP-AART) is the 28th lab to join ICAI. The lab will focus on the use of artificial intelligence for precision radiotherapy.

It is a major challenge to give patients the right dose of radiation, at the right spot with least damage to healthy tissue, and while the patient and the tumor move and change shape during radiation and over time. Within the POP-AART lab six PhD researchers will develop novel AI strategies for improving the images on which the radiation treatment is based, predicting changes over time of the tumor and incorporating them in automatic treatment planning and adaptation.

POP-AART will run for five years. Research topics range from improving CT images obtained just before radiation to the level of diagnostic quality CT images, predicting deformations and segmentations of the tumor and organs at risk and incorporate these data in online and automated treatment plan optimization for each patient individually at each radiation session.

The lab will be led by scientific directors Efstratios Gavves and Jan-Jakob Sonke. Gavves is assistant professor of Computer Vision and Deep Learning at UvA. Sonke is theme leader image guided therapy at the Netherlands cancer institute and Professor by special appointment of Adaptive Radiotherapy at the Faculty of Medicine at UvA. The Governing Board will consist of academic partners Lodewyk Wessels (NKI-AvL) and Mark de Graef (UvA) and industry partner Rui Lopes (Elekta).

About the Netherlands Cancer Institute

The Netherlands Cancer Institute, founded in 1913, is among the top 10 comprehensive cancer centers, combining world-class fundamental, translational, and clinical research with dedicated patient care. Their initiatives to promote excellent translational research have been recognized by the European Academy of Cancer Sciences, when they designated the institute ‘Comprehensive Cancer Center of Excellence in Translational Research’.

About Elektra

For almost five decades, Elekta has been a leader in precision radiation medicine. Their more than 4,000 employees worldwide are committed to ensuring everyone in the world with cancer has access to – and benefits from – more precise, personalized radiotherapy treatments. Headquartered in Stockholm, Sweden, Elekta is listed on NASDAQ Stockholm Exchange. 

Find out more about POP-AART lab.

Tilburg newest ICAI location with MasterMinds Lab

MindLabs recently joined the national Innovation Center for Artificial Intelligence (ICAI). This makes Tilburg one of eight locations with an ICAI lab. Together with the partners in the MasterMinds project, MindLabs collaborates in this public-private research lab, named MasterMinds AI Lab. The MasterMinds AI Lab is one of the many initiatives within MindLabs and is dedicated to the development and evaluation of new technologies, focusing on the cross-fertilization between artificial minds and human minds.

Artificial Intelligence and Human Behavior

The ICAI is a national collaboration of several universities, companies and the Dutch government with 27 labs on eight locations. The goal  of the Dutch government and universities is to remain at the forefront of AI by knowledge development and nurturing young talent.  MasterMinds collaborates in research at the intersection of robotics and avatars, serious gaming, decision making, and virtual and augmented reality. The results of these studies are applied to realize AI solutions for the benefit of society.

Max Louwerse

Professor Max Louwerse, scientific director of the MasterMinds AI Lab: “The future of AI increasingly lies at the intersection between artificial intelligence and human behavior. The MasterMinds project works at this interface to develop new technologies and will be able to apply them immediately. This is entirely in the nature of MindLabs.”

Five MasterMinds research projects

The MasterMinds project consists of five innovative research projects, aiming to develop breakthroughs with interactive AI technologies such as serious gaming, augmented and virtual reality, intelligent tutoring systems and natural language processing and data science. Research questions include: Can we train and improve complex decision-making using serious gaming? What is the learning effect of training pilots using virtual reality? How do we effectively design AR and VR training modules? How can we develop and use intelligent tutoring systems? The project is funded to stimulate regional ecosystems to develop a resilient, sustainable, and future-proof economy with a central role for SME’s.

The MasterMinds project brings together knowledge institutions, industrial partners, and governmental organizations to work together on AI solutions that can be readily used for the partners. The project aims to develop AI technologies combined with the impact on and input from human behavior, across multiple sectors in society in the field of aerospace, logistics, maintenance, and education focusing on robotics and avatars, serious gaming and learning, language and data science technologies and virtual and augmented reality solutions. The project provides a T-shaped profile of explainable AI solutions, where depth is achieved per subproject while breadth is achieved across the five projects. The project answers questions that have been formed by the industrial partners to prepare for the technology-driven future that lies ahead.

The five MasterMinds reseach projects:

  1. Serious Games in logistics – Port of Rotterdam
  2. VR for air force simulations – Dutch Royal Airforce and MultiSIM
  3. AR & VR for production and maintenance – Actemium, Marel en CastLab
  4. Evidence-based prevention: predictive analytics – Interpolis en Gemeente Tilburg
  5. Virtual Reality in Education – WPG Zwijsen, Spacebuzz en Timeaware

MasterMinds brings together the “brightest minds” in artifical and human intelligence.

Read more on the MasterMinds Lab

UvA, TU Delft and Booking.com launch Mercury Machine Learning Lab

UvA, TU Delft and Booking.com collaborate on research into better recommendation systems

In the Mercury Machine Learning Lab, researchers from the University of Amsterdam (UvA) and Delft University of Technology (TU Delft) will be working together with Booking.com on various improved recommendation systems. The collaboration provides the unique opportunity to test AI techniques in the real world, allowing new machine learning methods to be safely developed for wide application, for example in mobility, energy or healthcare.

Every day, millions of travellers from all over the world make multiple decisions on Booking.com related to their upcoming travel plans. With all of these taps and clicks on property photos and scrolling through search results, Booking.com naturally has a wealth of data insights to help the company make changes on the platform to improve the customer experience.In addition to the responsibility of handling all of this information securely and ethically, how do you analyse all of this data properly and continue to make useful recommendations for customers? Is what works well for a Dutch traveller equally as relevant for a traveller from Japan? And how do you ensure that customers continue to receive interesting travel recommendations that are relevant to them without getting stuck in a filter bubble?

On the road to even better recommendations

One way to understand what constitutes a good recommendation is looking at what previous travellers have chosen and the experiences that their choices yielded. Machine learning techniques are well suited to learning such connections and preferences. However, the problem is that the connections and preferences found in the data are not only informed by the choices of other travellers, but also by the suggestions and selections the system showed them. In the Mercury Machine Learning Lab, researchers from the University of Amsterdam (UvA) and Delft University of Technology will work together with data scientists from Booking.com to develop methods that will ensure that this type of bias is avoided and that the learned connections remain accurate in a new or different context.

From the classroom to real-time e-commerce
Joris Mooij, scientific director of the Mercury Machine Learning Lab at the UvA: ‘It’s a huge opportunity for us as researchers to have access to a live dataset of global data and be able to experiment on Booking.com’s platform.’ Frans Oliehoek and Matthijs Spaan, scientific directors from TU Delft, agree. Oliehoek: ‘By testing AI techniques in the real world, we can better understand the limitations of current state-of-the-art methods in the field of reinforcement learning, as well as improve the application of AI in practice.’ Spaan: ‘The lab’s focus on developing better algorithms for recommender systems is highly relevant to our society as these systems guide many of our digital interactions. By addressing fundamental AI challenges, the results of the lab will also be valuable in other domains.’

Onno Zoeter, Principal Data Scientist and Booking.com scientific director adds: ‘Unlike in a hospital scenario for example, where experimenting with different types of data-driven recommendation systems can have real life-and-death consequences for patients and their suggested treatment protocols, testing approaches, models and hypotheses with travel data from Booking.com doesn’t come with the same public health implications. This means we can safely test and develop new machine-learning methods together that also have a potential impact far beyond the trips booked on our platform.’

Learnings from other languages

Artificial intelligence and natural language processing are already used to perform many important tasks in different languages, such as categorising reviews and fraud detection. The researchers will look for ways of creating a system with multiple languages in which the smaller languages can benefit from what has been learned in models and rounds of experimentation with languages that are spoken more widely. This should enable Booking.com to support all 44 languages and dialects in which their platform is available to customers in various new contexts, even more quickly.

Nurturing Dutch talent

The Mercury Machine Learning Lab will be part of ICAI, the Innovation Center for Artificial Intelligence. The Lab will provide world-class opportunities for Machine Learning graduates to stay in the Netherlands and lead innovative research, keeping important talent connected to Dutch universities and industry.

In addition to the existing researchers, the Mercury Machine Learning Lab will comprise six PhD candidates and two postdocs who will work on six different projects related to bias and generalisation problems over the course of the next five years. They will spend two days a week in the office at Booking.com doing research and actively participating in related streams of experimentation to test their hypotheses.

Find out more about Mercury Machine Learning Lab

Start of new ICAI lab: AI for Oncology

The AI for Oncology Lab is a collaboration between the Netherlands Cancer Institute and the University of Amsterdam. Both institutes join forces in the development of AI algorithms to improve cancer treatment. Join the opening on 24 June 2021.

Sign up for the opening on 24 June

The AI for Oncology lab will officially and festively start and you are invited! After the registration you will receive a link.

Date: Thursday 24 June 2021
Time: 16.00 to 17.00 hrs
Register here for the digital opening

Improved cancer treatment

The goal of the collaboration between the Netherlands Cancer Institute (NKI) and University of Amsterdam (UvA) is improved cancer treatment through the aid of Artificial Intelligence. A lot of complex information is acquired from patients during and prior to the treatment through medical imaging, pathology, DNA, and so on. AI solution can assist medical specialists finding and applying the right treatment based on all this information. Moreover, AI algorithms have the potential to guide medical interventions accurately to the location of the tumor without damaging surrounding healthy tissue.

Through this collaboration, expertise in cancer research and AI technology blend together. The domains are represented by Clarisa Sánchez on behalf of the UvA, as AI&Health expert and Jonas Teuwen and Jan-Jakob Sonke on behalf of the NKI, specialized in oncology research and AI. The first goal is to conduct innovative scientific research. Subsequently, if successful, the lab aims to integrate the results in clinical practice.

The benefits of AI for cancer research

AI technology will play a bigger part in the future of cancer research. Machine learning algorithms can now perform certain tasks that used to require human intelligence. Algorithms based on artificial neural network are used in ‘Deep Learning’. Instead of relying on a programmer to explicitly tell the algorithm what to do, this network only requires an end goal and will learn how to achieve from many examples. The benefits of that is that this ‘Deep Learning’ algorithm can reach insights the programmer would never have considered.

In the case of image recognition for cancer detection, we can use a large database containing images of patients with and without cancer. The computer can use this data to learn and, if the database is sufficiently large, will be able to detect tumors even better than a specialist. In terms of personalized medicine, the images and other data can be used to better predict the most beneficial treatment option for every patient.

Image guided therapy aims to deliver therapeutic interventions with high accuracy. AI algorithms can be trained to automatically analyze medical images acquired during treatment and guide the intervention to the correct location.

In the lab, five research topics will be studied covering important aspects such as early diagnoses and image guided therapy.

Find out more about AI for Oncology lab