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Enoch Tetteh

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Multi-Domain Balanced Sampling Improves Out-of-Distribution Generalization of Chest X-ray Pathology Prediction Models

Dec 28, 2021
Enoch Tetteh, Joseph Viviano, Yoshua Bengio, David Krueger, Joseph Paul Cohen

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Learning models that generalize under different distribution shifts in medical imaging has been a long-standing research challenge. There have been several proposals for efficient and robust visual representation learning among vision research practitioners, especially in the sensitive and critical biomedical domain. In this paper, we propose an idea for out-of-distribution generalization of chest X-ray pathologies that uses a simple balanced batch sampling technique. We observed that balanced sampling between the multiple training datasets improves the performance over baseline models trained without balancing.

* MED-NEURIPS 2021 
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Multi-Domain Balanced Sampling Improves Out-of-Generalization of Chest X-ray Pathology Prediction Models

Dec 27, 2021
Enoch Tetteh, Joseph Viviano, Yoshua Bengio, David Krueger, Joseph Paul Cohen

Figure 1 for Multi-Domain Balanced Sampling Improves Out-of-Generalization of Chest X-ray Pathology Prediction Models
Figure 2 for Multi-Domain Balanced Sampling Improves Out-of-Generalization of Chest X-ray Pathology Prediction Models

Learning models that generalize under different distribution shifts in medical imaging has been a long-standing research challenge. There have been several proposals for efficient and robust visual representation learning among vision research practitioners, especially in the sensitive and critical biomedical domain. In this paper, we propose an idea for out-of-distribution generalization of chest X-ray pathologies that uses a simple balanced batch sampling technique. We observed that balanced sampling between the multiple training datasets improves the performance over baseline models trained without balancing.

* MED-NEURIPS 2021 
Viaarxiv icon