Ingénieur(e) Thèse Cifre (h/f): Observateurs Modèles Pilotés Par Ia Pour L’évaluation Des Performances Orientée-tâche En Tomosynthèse Mammaire Numérique

GE HealthCare UK

Buc, France
Deep learning model observers
Ai adaptive learning approach
Digital breast tomosynthesis (dbt)
This PhD project aims to develop and validate an AI-based adaptive learning approach to create domain-sensitive model observers capable of replicating human performance in digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM)

Job Summary

  • This PhD project aims to develop and validate an AI-based adaptive learning approach to create domain-sensitive model observers capable of replicating human performance in digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM).
  • The role involves designing and conducting experiments with human observers, training and calibrating models to replicate human performance using annotated datasets, and proposing, developing, and validating mathematical observers that mimic radiologist image reading.
  • GE HealthCare is a global leader in medical technology and digital solutions, enabling clinicians to make faster, more relevant decisions through intelligent equipment, data analytics, applications, and services.

Matching Summary

This PhD project aims to develop and validate an AI-based adaptive learning approach to create domain-sensitive model observers capable of replicating human performance in digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM).

Skills & Requirements

Must-have

  • Deep learning model observers
  • AI adaptive learning approach
  • Digital breast tomosynthesis (DBT)
  • Numerical breast tomosynthesis
  • Python and Unix/Linux mastery
  • Signal and image processing knowledge

Nice-to-have

  • Human observer studies
  • Psychophysics research interest
  • Cross-disciplinary team interaction
  • Passion for research applications

Key Requirements

  • Master's degree in Science or Engineering
  • Specialization in learning, data science, or applied mathematics
  • Experience with data science and learning methods
  • Excellent written and oral communication
  • Excellent English proficiency

Work Rights

Not specified

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