Research Assistant: Foundation Model Empowered Computational Pathology Images Analysis

vor 22 Stunden


Munich, Bayern, Deutschland Technical University of Munich Vollzeit
Master/Bachelor Thesis Opportunity

Foundation Model Empowered Computational Pathology Images Analysis

We are seeking a highly motivated research assistant to join our team at the Technical University of Munich and contribute to the evaluation of foundation model performances on downstream pathology image tasks.

Artificial intelligence has the potential to revolutionize cancer diagnosis and treatment by analyzing pathology images for precision medicine and decision support systems. However, AI still faces challenges in generalization and addressing rare diseases due to limited training data availability.

A foundation model may contribute to addressing this challenge. A foundation model refers to a general-purpose model pre-trained on typically unlabeled datasets, subsequently fine-tuned to apply to diverse downstream tasks. To compare the proposed foundation model with previous state-of-the-art methods, we want to evaluate the performance of patch/slide level classification and segmentation tasks.

Tasks:
  • Paper reading and literature review
  • Evaluate proprietary and public foundation models on public datasets
  • Finetune the models to obtain the best performance

We will be working with publicly available models: UNI, cTransPath, Virchow, RudolfV, and MSSM. The datasets for classification and detection include MHIST, PCAM, NCT-CRC, CAMELYON16/17, and for segmentation, SegPath and PanNuke.

Requirements:
  • Basic knowledge in at least one of the following areas: Pytorch and deep learning knowledge, Medical image analysis

Supervision and Contact: Prof. Peter Schueffler and Jingsong Liu will be the supervisors.



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