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---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
Radiography - Brain CT Image Caption and Region of Interest Detection
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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