Florence2-FT-Med-brain-6

Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

Visual Analysis of Brain CT Image and Region of Interest.

Model Usage

load model

from transformers import AutoModelForCausalLM, AutoProcessor

# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load fine-tuned model and processor
model_id_or_path = "mychen76/Florence2-FT-Med-brain-6"
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)

Inference

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 Image Caption

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

result

{'<CAPTION>': '<CAPTION>The image is a non-contrast computed tomography (CT) scan of the brain, showing the cerebral hemispheres without any medical devices present. The region of interest, located centrally and in the middle of the image, occupies approximately 1.5% of the area and appears to have a different density compared to the surrounding brain tissue, which may indicate an intracranial hemorrhage. This abnormal area could be related to the brain structures, potentially leading to a mass effect or displacement of adjacent brain tissue.'}

Task Image Caption Details

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

result

{'<CAPTION_DETAILS>': '<CAPTION_DETAILS>The image is a non-contrast computed tomography (CT) scan of the brain, showing the cerebral hemispheres without any medical devices present. The region of interest, located centrally and in the middle of the image, occupies approximately 1.5% of the area and appears to have a different density compared to the surrounding brain tissue, which may indicate an intracranial hemorrhage. This abnormality is likely related to the brain structures, potentially affecting them or being affected by a mass effect or displacement of adjacent brain tissue.'}

Task Region of Interest

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

result

{'<REGION_OF_INTEREST>': '<REGION_OF_INTEREST>The region of interest, located centrally and in the lower-middle area of 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 hemorrhage.\n<OBSERVATION>\nThis abnormality is likely related to the brain structures, potentially affecting them or being affected by a mass effect or displacement of adjacent brain tissue.'}

Task Json Record

results=run_model_inference("<JSON_RECORD>",None,test_image,device)
record=results["<JSON_RECORD>"].split("<JSON_RECORD>")[1]
record

result

"{'caption': 'The image is a non-contrast computed tomography (CT) scan of the brain, showing the cerebral hemispheres, ventricles, and subarachnoid spaces without any medical devices visible. The region of interest, located centrally and in the lower-middle area of the image, occupies approximately 1.5% of the area and appears to have a different density compared to the surrounding brain tissue, which may indicate an intracranial hemorrhage. This abnormal area could be related to or be affected by adjacent brain structures, potentially leading to a mass effect or displacement of adjacent brain tissue due to the presence of blood or a hematoma.'region of interest': 'This abnormal area is situated near the lateral ventricle, which is indicative of a hemorrhage, and could be exerting pressure or pressure on nearby brain structures due to its proximity to other cerebral structures.\\nThe relationship between this abnormal area and the surrounding cerebral structures suggests that the hemorrhage could be affecting or be affecting adjacent brain areas due to their proximity and displacement of nearby brain tissue.', 'observation': '', 'The abnormal area appears to be in a different area, possibly representing a hemorrhaged area, and is likely related to the adjacent brain regions, potentially affecting or being affected by nearby brain tissues due to it's proximity to nearby cerebral structures, although the relationship does not imply a significant difference in density or displacement within the brain tissue. 'bbox': '[212.7360076904297, 203.52000427246094, 252.16001892089844, 343.8080139160156]'}"

Model Description

Visual Analysis of Brain CT Image and Region of Interest.

<!-- 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]

Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

• Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

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 presented in Lacoste et al. (2019).

• 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

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

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:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]