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PhD Robust and reliable Artificial Intelligence for oncology

PhD Robust and reliable Artificial Intelligence for oncology

The Department of Electrical Engineering of Eindhoven University of Technology, in collaboration with Philips and Catharina Hospital Eindhoven, is looking for a PhD candidate for investigating Robust and reliable Artificial Intelligence for oncology.
Position
PhD-student
Department(s)
Electrical Engineering
Graduate Program(s)
Electrical Engineering
FTE
1,0
Date off
30/09/2021
Reference number
V36.5159

Job description

Background

Early detection, accurate diagnosis/staging of cancer, and subsequent selection of the appropriate treatment are critical factors to improve patient outcomes. Diagnostic imaging plays an integral role in many phases of a patient’s care path, but considerable challenges remain regarding the accuracy, efficacy and efficiency. Artificial Intelligence (AI) holds great promise to support image analysis to detect, characterize, and monitoring disease. AI can enable automated segmentation and support in diagnosis (e.g. classifying abnormalities as benign or malignant) and staging (categorizing tumors into pre-defined groups based on expected disease course and treatment strategy). Furthermore, it can support cancer monitoring by capturing image features over time to evaluate the patient’s response to treatment.

Many AI solutions for radiology have been developed in research labs, but few have been adopted successfully in clinical practice, limiting their impact on patient outcomes. A well-recognized cause for this poor adoption is a lack of human-centered design of AI, leading to solutions to clinically insignificant problems, too much or too little trust in AI outcomes and poor fit of AI solutions in clinician’s ways of working. This project will therefore develop tools, methods and assets to bring AI to clinical practice and evaluate actual use, trust, and experience with AI. The design of optimal physician-AI collaboration combines data science and UI/UX expertise to address technical challenges, such as AI explainability, robustness and uncertainty.

Advancing Cancer care through Interpretable AI

The Eindhoven MedTech Innovation Center (e/MTIC) is a public-private partnership aimed at creating and growing an ecosystem that offers a fast track to high-tech health innovations. Within this ecosystem, we aim to create impactful innovations in the oncology domain, through close multidisciplinary collaboration between an academic partner (Eindhoven University of Technology), a top-clinical hospital (Catharina Hospital Eindhoven) and an industrial partner (Philips). In this triangle, the research project “Advancing Cancer care through Interpretable AI” (ACACIA) aims to advance clinical decision support for cancer care with Artificial Intelligence (AI), taking a patient-centered and physician-centered approach to optimize physician-AI collaboration and adoption of AI in clinical practice. This project focusses on the integration of AI in CT-based imaging systems and builds on prior work that has been developed within e/MTIC in recent years.

You will be part of an innovative multi-disciplinary team

This PhD position is embedded in a multidisciplinary team of in total 4 PhD positions covering machine learning, product design and clinical expertise. The position advertised here focusses on development of the machine learning models. Around this core team, a group of domain experts is closely involved in the project, supporting the PhD researchers. This group consists of medical specialists such as radiologists and surgeons, but also experienced research scientists from Philips and Eindhoven University of Technology. The project activities are organized in 3 tightly interlinked workpackages:

WP1. Machine Learning for robust & trustworthy AI

This activity, led by the machine learning PhD student, aims to develop robust and trustworthy AI in medical imaging. It will address a subset of clinical application areas (e.g., lung cancer and pancreas cancer), by development and maturation of AI methods for the selected clinical use cases. Research topics include, but are not limited to, AI transparency, interpretability and explainability, self-critical AI, confidence quantification and out-of-distribution detection. A more detailed description is provided below.

WP2. User experience and interface design and evaluation for optimal physician-AI collaboration

This activity, led by the design PhD student, aims to iteratively develop and evaluate UI/UX innovations to shape optimal physician-AI collaboration, as well as methods to evaluate the clinical experience with end-users. This will drive the AI models developed in WP1 and integrate AI-based outputs generated in WP1.

WP3: Clinical data collection, user testing and evaluation

In this activity, led by the two clinical PhD students, the AI algorithms developed in WP1 and clinician-AI collaboration innovations developed in WP2 will be evaluated in clinical practice. These comprise the evaluation of AI-based lung nodule assessment, evaluation of AI-based pancreatic cancer detection and resectability assessment and testing of the scalability of the innovations to a third cancer type (e.g. other abdominal cancer such as kidney cancer).

The 4 PhD candidates will closely collaborate and share data, AI models, technological platforms and methodologies as complementary focus points of each PhD position.

Machine Learning for robust & trustworthy AI

We are interested in how we can efficiently develop trustworthy models for medical image analysis that can deal with the enormous amount of heterogeneity in the visual representation of tumors and how we can efficiently translate AI solutions to other CT applications for oncology, e.g. from pancreatic cancer other abdominal cancers (e.g. kidney). The PhD position advertised here, focusses on the development and thorough evaluation of machine learning methods for analysis of Computed Tomography (CT) scans. While off-the-shelf AI models, e.g. various types of Convolutional Neural Networks (CNNs) will be implemented and benchmarked, this project aims to move beyond the state-of-the-art AI solutions, with a special focus on explainability, uncertainty and robustness. These aspects are key for clinical integration and an optimal collaboration between the medical specialist and the AI system, fostering the adoption of AI in clinical practice. Specific tasks towards this goal include:

  • Domain-specific (un-/semi-supervised) pre-training with large sets of medical data;
  • Combining methods from signal processing with CNNs to enhance efficiency and interpretability;
  • Exploring methods to quantify different forms of uncertainty;
  • Developing tools to reveal the visual clues that lead to a certain AI prediction.

Job requirements

The envisioned candidate for the open position holds an MSc degree in a relevant field for this project (e.g. Electrical engineering, Computer science, Applied Mathematics, Artificial Intelligence, …) with a strong background in machine learning, computer vision and medical image processing and a drive to improve healthcare. The candidate should have:

  • Solid understanding of convolutional neural networks and basic knowledge of signal/image  processing;
  • Basic understanding of conventional machine learning approaches and their efficient implementation;
  • Strong communication skills, including an excellent proficiency in English (spoken and written);
  • Solid coding skills (e.g Matlab, Python and/or C++);
  • Experience with commonly used deep learning frameworks (TensorFlow/Keras or PyTorch);
  • An affinity with the field of medicine and in particular oncology;
  • Eager to spend part of your time on-site with each partner (TU/e, Philips, Catharina Hospital) so as to fully apprehend and profit from the expertise, capabilities and facilities of each partner (all partners are located in close proximity to each other).

For this project, some additional points are desired and could break the tie for two equally suited candidates. These properties are:

  • Knowledge of signal transformations, information theory and medical image processing;
  • Experience with Bayesian neural networks and/or Normalizing flows;
  • Some medical background, either from education (e.g. technical medicine) or previous projects.

Conditions of employment

  • Envisioned starting date of PhD project preferably September/October 2021.
  • A meaningful job in a dynamic and ambitious university with the possibility to present your work at international conferences.
  • A full-time employment for four years, with an intermediate evaluation after one year.
  • To support you during your PhD and to prepare you for the rest of your career, you will have free access to a personal development program for PhD students (PROOF program).
  • A gross monthly salary of € 2.395,00 in the first year and € 3.061,00 in the last year (on a fulltime basis). The salary is in accordance with the Collective Labor Agreement of the Dutch Universities.
  • Benefits in accordance with the Collective Labor Agreement for Dutch Universities.
  • Additionally, an annual holiday allowance of 8% of the yearly salary, plus a year-end allowance of 8.3% of the annual salary.
  • A broad package of fringe benefits, including an excellent technical infrastructure, moving expenses, and savings schemes.
  • Family-friendly initiatives are in place, such as an international spouse program, and excellent on-campus children day care and sports facilities

Information and application

Do you recognize yourself in this profile and would you like to know more?
Please contact  Dr Fons van der Sommen (fvdsommen[at]tue.nl).

For information about terms of employment, click here or contact HRServices.flux[at}]tue.nl.

Please visit www.tue.nl/jobs to find out more about working at TU/e!

We invite you to submit a complete application by using the 'apply now' button on this page. The application should include:

  • Cover letter in which you describe your motivation and qualifications for the position (clearly indicate which position you apply for).
  • Submit you curriculum vitae, including a list, if any, of your publications and the contact information of two references.
  • A copy of your Masters Thesis
  • Copies of diplomas and course grades

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We look forward to your application and will screen it as soon as we have received it. The screening will continue until the position has been filled.