Medgemma Histopathology Lung Colon

--- base_model: google/medgemma-4b-it library_name: transformers model_name: medgemma-Histopathology-lung-colon tags: - generated_from_trainer - trl - sft licence: license license: mit language: - en metrics: - f1 - accuracy - precision - recall pipeline_tag: visual-question-answering --- # Model Card for medgemma-Histopathology-lung-colon This model is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it). It has been trained using [Fine Tuning script. Kindly download and use it may give you error while previewing](https://github.com/gowthambalachandhiran/MedGemmaAUKBC/blob/main/notebook/Fine_Tuning_Medgemma.ipynb). ## Quick start ```python from transformers import pipeline from PIL import Image import requests import torch pipe = pipeline( "image-text-to-text", model="gbalachandhiran/medgemma-Histopathology-lung-colon", torch_dtype=torch.bfloat16, device="cuda", ) ## Lung cancer sample image_url = "https://cdnintech.com/media/chapter/59926/1512345123/media/F1.png" image = Image.open(requests.get(image_url, headers={"User-Agent": "example"}, stream=True).raw) TISSUE_CLASSES = [ "A: colon_aca", "B: colon_n", "C: lung_aca", "D: lung_n", "E: lung_scc" ] options = "\n".join(TISSUE_CLASSES) PROMPT = f"What is the most likely tissue type shown in the histopathology image?\n{options}"'' messages = [ { "role": "system", "content": [{"type": "text", "text": "You are an expert radiologist."}] }, { "role": "user", "content": [ {"type": "text", "text": PROMPT}, {"type": "image", "image": image}, ] } ] output = pipe(text=messages, max_new_tokens=50) print(output[0]["generated_text"][-1]["content"]) ``` ## Training procedure This is a finetuned model from original Medgemma 4B model Fine tuning notebook : ### Framework versions - TRL: 0.19.0 - Transformers: 4.53.0 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.2 ## Citations Dataset: https://www.kaggle.com/datasets/andrewmvd/lung-and-colon-cancer-histopathological-images Credited Authors of the Dataset : Borkowski AA, Bui MM, Thomas LB, Wilson CP, DeLand LA, Mastorides SM. Lung and Colon Cancer Histopathological Image Dataset (LC25000). arXiv:1912.12142v1 [eess.IV], 2019 Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{Fine Tuning Medgemma to identify Histopathology images of lungs and Colon}}, author = {Dr. Ramanan. R...