ViT-Breast-Cancer

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on a dataset of breast cancer microscope slides.

Model description

This is a fine-tuned ViT (Google) that serves more as an exploration of vision transformers in medicine for my learning than as anything specific. I fine-tuned this model on a dataset of ~7000 images of breast cancer slides labelled as 'benign' or 'cancerous'. I used the Transformers library and the out-of-the-box ViTForImageClassification configuration. Despite this being an incredibly barebones fine-tune, I hope you fine it useful! Any recommendations are welcome!

Intended uses & limitations

This is a super basic fine tuned model. Please evaluate its performance for yourself do determine whether it can be useful for you. In a big picture sense, this model can tell apart benign and cancerous breast tissue samples.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0002
• 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

Training results

• training_loss = 0.007100

Framework versions

• Transformers 4.42.0.dev0
• Pytorch 2.3.0+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1