🖼️ 该教程演示了如何使用TorchXRayVision库加载预训练的DenseNet模型，该模型用于分析X光图像。

💻 使用Gradio创建交互式用户界面，用户可以上传胸部X光图像并获得分类判断。

⚙️ 教程详细介绍了图像预处理、模型推断和结果解释的步骤，所有操作都在Google Colab上进行，无需额外的设置。

⚠️ 强调该工具仅用于教育目的，不适用于临床诊断，并鼓励用户在此基础上进行开发，但需严格遵守医疗标准。

In this tutorial, we demonstrate how to build a prototype X-ray judgment tool using open-source libraries in Google Colab. By leveraging the power of TorchXRayVision for loading pre-trained DenseNet models and Gradio for creating an interactive user interface, we show how to process and classify chest X-ray images with minimal setup. This notebook guides you through image preprocessing, model inference, and result interpretation, all designed to run seamlessly on Colab without requiring external API keys or logins. Please note that this demo is intended for educational purposes only and should not be used as a substitute for professional clinical diagnosis.

!pip install torchxrayvision gradio

First, we install the torchxrayvision library for X-ray analysis and Gradio to create an interactive interface.

import torchimport torchxrayvision as xrvimport torchvision.transforms as transformsimport gradio as gr

We import PyTorch for deep learning operations, TorchXRayVision for X‑ray analysis, torchvision’s transforms for image preprocessing, and Gradio for building an interactive UI.

model = xrv.models.DenseNet(weights="densenet121-res224-all")model.eval()  

Then, we load a pre-trained DenseNet model using the “densenet121-res224-all” weights and set it to evaluation mode for inference.

try:    pathology_labels = model.meta["labels"]    print("Retrieved pathology labels from model.meta.")except Exception as e:    print("Could not retrieve labels from model.meta. Using fallback labels.")    pathology_labels = [         "Atelectasis", "Cardiomegaly", "Consolidation", "Edema",         "Emphysema", "Fibrosis", "Hernia", "Infiltration", "Mass",         "Nodule", "Pleural Effusion", "Pneumonia", "Pneumothorax", "No Finding"    ]

Now, we attempt to retrieve pathology labels from the model’s metadata and fall back to a predefined list if the retrieval fails.

def classify_xray(image):    try:        transform = transforms.Compose([            transforms.Resize((224, 224)),            transforms.Grayscale(num_output_channels=1),            transforms.ToTensor()        ])        input_tensor = transform(image).unsqueeze(0)  # add batch dimension        with torch.no_grad():            preds = model(input_tensor)               pathology_scores = preds[0].detach().numpy()        results = {}        for idx, label in enumerate(pathology_labels):            results[label] = float(pathology_scores[idx])               sorted_results = sorted(results.items(), key=lambda x: x[1], reverse=True)        top_label, top_score = sorted_results[0]               judgement = (            f"Prediction: {top_label} (score: {top_score:.2f})nn"            f"Full Scores:n{results}"        )        return judgement    except Exception as e:        return f"Error during inference: {str(e)}"

Here, with this function, we preprocess an input X-ray image, run inference using the pre-trained model, extract pathology scores, and return a formatted summary of the top prediction and all scores while handling errors gracefully.

iface = gr.Interface(    fn=classify_xray,    inputs=gr.Image(type="pil"),    outputs="text",    title="X-ray Judgement Tool (Prototype)",    description=(        "Upload a chest X-ray image to receive a classification judgement. "        "This demo is for educational purposes only and is not intended for clinical use."    ))iface.launch()

Finally, we build and launch a Gradio interface that lets users upload a chest X-ray image. The classify_xray function processes the image to output a diagnostic judgment.

Gradio Interface for the tool

Through this tutorial, we’ve explored the development of an interactive X-ray judgment tool that integrates advanced deep learning techniques with a user-friendly interface. Despite the inherent limitations, such as the model not being fine-tuned for clinical diagnostics, this prototype serves as a valuable starting point for experimenting with medical imaging applications. We encourage you to build upon this foundation, considering the importance of rigorous validation and adherence to medical standards for real-world use.

Here is the Colab Notebook. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Group. Don’t Forget to join our 85k+ ML SubReddit.

The post How to Build a Prototype X-ray Judgment Tool (Open Source Medical Inference System) Using TorchXRayVision, Gradio, and PyTorch appeared first on MarkTechPost.