mychen76/Florence2-FT-Med-brain-408

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Radiography - Brain CT Image Caption and Region of Interest Detection

Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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Usage

import os from datasets import load_dataset

Load dataset

ds = load_dataset("mychen76/medtrinity_brain_408_hf")
train=ds["train"]

idx=20
test_image = test_ds[idx]["image"]
test_image.resize([350, 350])

Load Model

import torch
from PIL import Image
import matplotlib.pyplot as plt
import textwrap
from transformers import AutoModelForCausalLM, AutoProcessor

# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
mode_id_or_path = "mychen76/Florence2-FT-Med-brain-408"

# Load fine-tuned model and processor
model = AutoModelForCausalLM.from_pretrained(model_id_or_path, trust_remote_code=True).to(device)
processor = AutoProcessor.from_pretrained(model_id_or_path, trust_remote_code=True)

Test Model

# Function to run the model on an example
def run_model_inference(task_prompt, text_input, image, device="cpu"):

    if text_input is None:
        prompt = task_prompt
    else:
        prompt = task_prompt + text_input

    # print("PROMPT=",prompt)
    # Ensure the image is in RGB mode
    if image.mode != "RGB":
        image = image.convert("RGB")

    inputs = processor(text=prompt, images=image, return_tensors="pt").to(device)
    generated_ids = model.generate(
        input_ids=inputs["input_ids"],
        pixel_values=inputs["pixel_values"],
        max_new_tokens=1024,
        num_beams=3
    )
    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
    parsed_answer = processor.post_process_generation(generated_text, task=task_prompt, image_size=(image.width, image.height))
    return parsed_answer

Task-1 CAPTION

results = run_model_inference("<CAPTION>",None,test_image)
print(results)

Results

<CAPTION>The image is a non-contrasted CT scan of the brain, showing the abnormal abnormal density, located approximately 1.5% of the image area and appears to have a different density compared to the surrounding brain tissue, which may indicate an intracranial pressure. The region of interest, located adjacent to the adjacent brain, is indicative of a brain tissue. This abnormal area could be related to the brain structures due to the presence of blood or a mass effect, which is a common feature of adjacent brain structures.'

Task-2 CAPTION_DETAILS

results = run_model_inference("<CAPTION_DETAILS>",None,test_image)
print(results)

Results

<CAPTION_DETAILS>The image is a non-contrasted CT scan of the brain, showing the intracranial structures without any medical devices present.\n\nREGION OF INTEREST\nThe region of interest, located brain tissue, occupies approximately 1.5% of the image area and appears to have a different density compared to the surrounding brain tissue.\nCONDITION\nThis region's proximity to other brain structures could be related to a mass effect or as a result of a massage, which is indicative of a intracronial pressure.\nThis abnormal area could be indicative of an abnormal area, potentially potentially leading to a potential mass effect on adjacent brain structures.

Task-3 REGION_OF_INTEREST

results = run_model_inference("<REGION_OF_INTEREST>",None,test_image)
print(results)

Results

<REGION_OF_INTEREST>The region of interest, located adjacent to the brain, occupies approximately 1.5% of the image area and appears to have a different density compared to the surrounding brain tissue, which may indicate an intracranial pressure.

Task-4 OBSERVATION

results = run_model_inference("<REGION_OF_INTEREST>",None,test_image)
print(results)

Results

<OBSERVATION>The region of interest, located approximately 1.5% of the image area and appears to have a different density compared to the surrounding brain tissue, which may indicate an intracranial pressure.

Testing Data, Factors & Metrics

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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