license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-PICAI
results: []
vit-base-PICAI
This model is a fine-tuned version of google/vit-base-patch16-224 on the PICAI dataset. PI-CAI (Prostate Imaging: Cancer AI) is an all-new grand challenge, with over 10,000 carefully-curated prostate MRI exams to validate modern AI algorithms and estimate radiologists’ performance at csPCa detection and diagnosis. Key aspects of the study design have been established in conjunction with an international, multi-disciplinary scientific advisory board (16 experts in prostate AI, radiology and urology) —to unify and standardize present-day guidelines, and to ensure meaningful validation of prostate-AI towards clinical translation (Reinke et al., 2022). More can be found at the official Grand Channel Website: https://pi-cai.grand-challenge.org
It achieves the following results on the evaluation set:
- Loss: 0.6043
- Accuracy: 0.7371
- Roc Auc: 0.7059
Model description
More information needed
Intended uses & limitations
This model is just a test of how ViT perform with basic fine tuning over a challengin medical imaging dataset, and also to assess the explanation properties of ViT by looking at attention matrices produced by the model.
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Roc Auc |
|---|---|---|---|---|---|
| 0.4995 | 0.14 | 50 | 0.5423 | 0.7371 | 0.7072 |
| 0.4729 | 0.29 | 100 | 0.6259 | 0.7314 | 0.7183 |
| 0.5558 | 0.43 | 150 | 0.5564 | 0.7243 | 0.7189 |
| 0.5825 | 0.57 | 200 | 0.5912 | 0.6943 | 0.7177 |
| 0.5091 | 0.71 | 250 | 0.5656 | 0.73 | 0.7140 |
| 0.4575 | 0.86 | 300 | 0.5846 | 0.7386 | 0.6858 |
| 0.5168 | 1.0 | 350 | 0.5363 | 0.7471 | 0.7076 |
| 0.5305 | 1.14 | 400 | 0.5600 | 0.7357 | 0.7042 |
| 0.4275 | 1.29 | 450 | 0.5864 | 0.7357 | 0.6988 |
| 0.5588 | 1.43 | 500 | 0.5477 | 0.75 | 0.7078 |
| 0.4573 | 1.57 | 550 | 0.5321 | 0.7571 | 0.7253 |
| 0.5094 | 1.71 | 600 | 0.5840 | 0.7457 | 0.7054 |
| 0.5311 | 1.86 | 650 | 0.5719 | 0.7229 | 0.7098 |
| 0.4582 | 2.0 | 700 | 0.5439 | 0.7357 | 0.7062 |
| 0.5142 | 2.14 | 750 | 0.6668 | 0.6629 | 0.6899 |
| 0.3833 | 2.29 | 800 | 0.5705 | 0.7286 | 0.6954 |
| 0.4676 | 2.43 | 850 | 0.6152 | 0.6943 | 0.6795 |
| 0.4682 | 2.57 | 900 | 0.5679 | 0.7443 | 0.7077 |
| 0.4112 | 2.71 | 950 | 0.5600 | 0.7329 | 0.7073 |
| 0.5107 | 2.86 | 1000 | 0.5686 | 0.7343 | 0.7017 |
| 0.4078 | 3.0 | 1050 | 0.6165 | 0.7429 | 0.7168 |
| 0.479 | 3.14 | 1100 | 0.5952 | 0.7257 | 0.7004 |
| 0.3704 | 3.29 | 1150 | 0.5937 | 0.7314 | 0.6980 |
| 0.3733 | 3.43 | 1200 | 0.5923 | 0.7214 | 0.7001 |
| 0.3682 | 3.57 | 1250 | 0.6183 | 0.7429 | 0.6963 |
| 0.3283 | 3.71 | 1300 | 0.6130 | 0.73 | 0.7012 |
| 0.3709 | 3.86 | 1350 | 0.6123 | 0.74 | 0.7045 |
| 0.3859 | 4.0 | 1400 | 0.6043 | 0.7371 | 0.7059 |
Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2