MSc Thesis: Contrastive Pre-Training for Radiology Reports
In recent years transformer-based language models have proven quite successful in the field of natural language processing (NLP). These models require huge amounts of training data and are therefore typically pre-trained on unlabelled datasets using self-supervised objectives like masked language modelling (MLM) as proposed in BERT . While models like BioBERT  are pre-trained on the medical domain, the used pre-training objectives like MLM treat text as independent sentences and do not utilise the structure of medical documents. In this project we instead make use of the semi-structured nature of radiology reports and apply contrastive methods on the sections of these reports. Your task is the adaptation of such contrastive methods (e.g. SimCLR , BYOL , DINO , …) to be used effectively on language models.
What we offer
- Close supervision and access to state-of-the-art computer hardware
- A strong research group with lots of practical experience
- Cutting-edge research in Medical NLP with the opportunity to publish your work
- Advanced programming skills in Python and deep learning frameworks like PyTorch, JAX, or Tensorflow
- Strong background in deep learning, preferable (but not required) with experience in NLP
- Basic familiarity with self-supervised methods like SimCLR is preferable but not required
-  J. Devlin et al. “Bert: Pre-training of deep bidirectional transformers for language understanding.” arXiv preprint [arXiv:1810.04805] (2018).
-  J. Lee et al. “BioBERT: a pre-trained biomedical language representation model for biomedical text mining.” Bioinformatics 4.36 [link] (2020)
-  T. Chen et al. “Big Self-Supervised Models are Strong Semi-Supervised Learners.” NeurIPS [arXiv:2006.10029] (2020)
-  J. Grill et al. “Bootstrap Your Own Latent A New Approach to Self-Supervised Learning.” NIPS [link] (2020)
-  M. Caron et al. “Emerging Properties in Self-Supervised Vision Transformers.” ICCV [arXiv:2104.14294] (2021)