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Showing posts from January 14, 2023

RadImageNet: Can it be a Better Way to Train a Medical Imaging Machine Learning Model

 Training a Convolutional Neural Network (CNN) for performing medical images/ imaging tasks has always been done using Transfer learning as any other natural image classification task (cats Vs dogs). In other words, to build a medical image classification artificial intelligence model (disease detection, cancer malignancy identification, chest x-ray anomaly detection) we still fine-tune a pre-trained model such as InceptionV3 network, which was trained originally on ImageNet, on another medical dataset. So the issue here is we are fine-tuning a model, for performing a medical image classification task, that was originally trained on natural images like ImageNet . But, have you asked yourselves what can happen if we train the original model on medical images dataset similar to ImageNet, and then we fine-tune it on a specific dataset when needed (also medical)!.  RadImageNet The answer to the question above is that pretraining and fine-tuning using medical images instead of ImageNet can

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