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Numrering	Referens	Omslagsbild	Hitta
1.	Matsoukas, Christos, et al. (författare) What Makes Transfer Learning Work for Medical Images : Feature Reuse & Other Factors 2022 Ingår i: 2022 IEEE/CVF conference on computer vision and pattern recognition (CVPR). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 9215-9224 Konferensbidrag (refereegranskat)abstract Transfer learning is a standard technique to transfer knowledge from one domain to another. For applications in medical imaging, transfer from ImageNet has become the de-facto approach, despite differences in the tasks and image characteristics between the domains. However, it is unclear what factors determine whether - and to what extent transfer learning to the medical domain is useful. The longstanding assumption that features from the source domain get reused has recently been called into question. Through a series of experiments on several medical image benchmark datasets, we explore the relationship between transfer learning, data size, the capacity and inductive bias of the model, as well as the distance between the source and target domain. Our findings suggest that transfer learning is beneficial in most cases, and we characterize the important role feature reuse plays in its success.

Matsoukas, Christos, et al. (författare)
What Makes Transfer Learning Work for Medical Images : Feature Reuse & Other Factors
2022
Ingår i: 2022 IEEE/CVF conference on computer vision and pattern recognition (CVPR). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 9215-9224
Konferensbidrag (refereegranskat)abstract
- Transfer learning is a standard technique to transfer knowledge from one domain to another. For applications in medical imaging, transfer from ImageNet has become the de-facto approach, despite differences in the tasks and image characteristics between the domains. However, it is unclear what factors determine whether - and to what extent transfer learning to the medical domain is useful. The longstanding assumption that features from the source domain get reused has recently been called into question. Through a series of experiments on several medical image benchmark datasets, we explore the relationship between transfer learning, data size, the capacity and inductive bias of the model, as well as the distance between the source and target domain. Our findings suggest that transfer learning is beneficial in most cases, and we characterize the important role feature reuse plays in its success.

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