Learning to learn: Which data we have to annotate first in medical applications?
Supervision: Prof. Dr. Nassir Navab, Dr. Seong Tae KimAbstract
Although the semi-supervised or unsupervised learning has been developed recently, the performance of them is still bound to the performance of fully-supervised learning. However, the cost of the annotation is extremely high in medical applications. It requires medical specialists (radiologists or pathologists) required to annotate the data. For those reasons, it is almost impossible to annotate all available dataset and sometimes, the only a subset of a dataset is possible to be selected for annotation due to the limited budget. Active learning is the research field which tries to deal with this problem [1-5]. Previous studies have been conducted in mainly three approaches: an uncertainty-based approach, a diversity-based approach, and expected model change [3]. These studies have been verified that active learning has the potential to reduce annotation cost. In this project, we aim to propose a novel active learning method which learns a simple uncertainty calculator to select more informative data to learn the current deep neural networks in medical applications.Requirements:
- Good understanding of statistics and machine learning methods.
- Very good programming skills in Python & TensorFlow? / PyTorch?
Location:
- Garching
Resultant Paper
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S.T. Kim, F. Mustaq, N. Navab
Confident Coreset for Active Learning in Medical Image Analysis The first two authors contributed equally. arXiv:2004.02200. (bib) |
Acknowledgement:
This project is based upon work supported by Google Cloud.| ProjectForm | |
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| Title: | Learning to learn: Which data we have to annotate first in medical applications? |
| Abstract: | Although the semi-supervised or unsupervised learning has been developed recently, the performance of them is still bound to the performance of fully-supervised learning. However, the cost of the annotation is extremely high in medical applications. It requires medical specialists (radiologists or pathologists) required to annotate the data. For those reasons, it is almost impossible to annotate all available dataset and sometimes, the only a subset of a dataset is possible to be selected for annotation due to the limited budget. Active learning is the research field which tries to deal with this problem [1-5]. Previous studies have been conducted in mainly three approaches: an uncertainty-based approach, a diversity-based approach, and expected model change [3]. These studies have been verified that active learning has the potential to reduce annotation cost. In this project, we aim to propose a novel active learning method which learns a simple uncertainty calculator to select more informative data to learn the current deep neural networks in medical applications. |
| Student: | Farrukh Mushtaq |
| Director: | Prof. Dr. Nassir Navab |
| Supervisor: | Dr. Seong Tae Kim |
| Type: | Master Thesis |
| Area: | Segmentation, Machine Learning, Medical Imaging, Computer Vision |
| Status: | finished |
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