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A British researcher is facing a challenge with their machine learning model, specifically overfitting on a dataset of 2D X-ray angiograms used for classifying coronary artery blocks (LCA vs. RCA).
- The researcher is working on a binary classification problem related to coronary arteries.
- They are using InceptionV3 architecture in PyTorch and have experimented with transfer learning from ImageNet.
- The dataset consists of approximately 900 training frames, derived from around 300 unique DICOMs.
Despite employing various strategies such as normalization, class weights adjustment, dropout regularization, weight decay, data augmentation, and scheduler modifications for early stopping, the model continues to overfit, achieving high accuracy on the training set but poor performance on unseen validation data. This indicates that despite efforts to mitigate overfitting, the current approach is insufficient.
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### Takeaways:
– The researcher is dealing with a small dataset in a medical imaging context, which can be particularly challenging for avoiding overfitting.
– Overfitting remains a key issue even with advanced techniques like transfer learning and various regularization methods.
– Further exploration of more robust augmentation strategies or data augmentation pipelines might be necessary to improve model generalization.
Originally published at reddit.com. Curated by AI Maestro.
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