| --- |
| license: mit |
| pipeline_tag: image-classification |
| tags: |
| - medmnist |
| - pathmnist |
| - medical |
| --- |
| |
| # MAE-Model-MedMNIST-Predictor |
| Using Masked Autoencoder model pretraining to Predict Medical Imaging Results. |
|
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| # For Code of the Model Architecture and Training: |
| [Github Model Code](https://github.com/HasanAli5/MAE-Model-MedMNIST-Predictor) |
|
|
| ## For Live Demo: |
| [Hugging Face Model Space Demo](https://huggingface.co/spaces/Hali5/MAE-PathMNIST-Predictor-Demo) |
|
|
| ## Results: |
|
|
| The best predictor variant was the cross attention pooler with 95.11% test accuracy. |
|
|
| This test accuracy beats the best ResNet (ResNet-50 (28)) model accuracy from the official MedMNIST v2 dataset benchmark by 4%. |
|
|
| Here are the variants and their test accuracies and AUCs: |
|
|
| | Variant | Accuracy | AUC | |
| |--------------------------|--------------------|-------------------| |
| | Benchmark ResNet-50 (28) | 0.911 | 0.990 | |
| | Linear Probe (GAP) | 0.9415 | 0.9942 | |
| | Cross Attention Pooler | **0.9511** | 0.9942 | |
| | Cross Attention Hybrid | 0.9433 | **0.9954** | |
|
|
