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.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
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+
+# PyBuilder
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+
+# Jupyter Notebook
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+
+# IPython
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+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
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+ENV/
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+
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+
+# Rope project settings
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+
+# mkdocs documentation
+/site
+
+# mypy
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+
+# Pyre type checker
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+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

README.md

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----
-title: Chest Xray Analyzer Using Ai
-emoji: 📉
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 5.49.0
-app_file: app.py
-pinned: false
-short_description: Analyze chest x ray image using ai
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: AI for Chest X-rays
+app_file: app.py
+sdk: gradio
+sdk_version: 5.9.0
+---

app.py

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+import cv2
+import gradio as gr
+import numpy as np
+import spaces
+import torch
+import torch.nn.functional as F
+
+from einops import rearrange
+
+from transformers import AutoModel
+
+
+def calculate_ctr(mask: np.ndarray) -> float:
+    # mask.ndim = 2, (height, width)
+    lungs = np.zeros_like(mask)
+    lungs[mask == 1] = 1
+    lungs[mask == 2] = 1
+    heart = (mask == 3).astype("int")
+    y, x = np.stack(np.where(lungs == 1))
+    lung_min = x.min()
+    lung_max = x.max()
+    y, x = np.stack(np.where(heart == 1))
+    heart_min = x.min()
+    heart_max = x.max()
+    lung_range = lung_max - lung_min
+    heart_range = heart_max - heart_min
+    return heart_range / lung_range
+
+
+def make_overlay(
+    img: np.ndarray, mask: np.ndarray, alpha: float = 0.7
+) -> np.ndarray[np.uint8]:
+    overlay = alpha * img + (1 - alpha) * mask
+    return overlay.astype(np.uint8)
+
+
+@spaces.GPU
+def predict(Radiograph):
+    rg = cv2.cvtColor(Radiograph, cv2.COLOR_GRAY2RGB)
+    x = cxr_info_model.preprocess(Radiograph)
+    x = torch.from_numpy(x).float().to(device)
+    x = rearrange(x, "h w -> 1 1 h w")
+
+    with torch.inference_mode():
+        info_out = cxr_info_model(x)
+
+    info_mask = info_out["mask"]
+    h, w = rg.shape[:2]
+    info_mask = F.interpolate(info_mask, size=(h, w), mode="bilinear")
+    info_mask = info_mask.argmax(1)[0]
+    info_mask_3ch = F.one_hot(info_mask, num_classes=4)[..., 1:]
+    info_mask_3ch = (info_mask_3ch.cpu().numpy() * 255).astype(np.uint8)
+    info_overlay = make_overlay(rg, info_mask_3ch[..., ::-1])
+
+    view = info_out["view"].argmax(1).item()
+    info_string = ""
+    if view in {0, 1}:
+        info_string += "This is a frontal chest radiograph "
+        if view == 0:
+            info_string += "(AP projection)."
+        elif view == 1:
+            info_string += "(PA projection)."
+    elif view == 2:
+        info_string += "This is a lateral chest radiograph."
+
+    age = info_out["age"].item()
+    info_string += f"\nThe patient's predicted age is {round(age)} years."
+    sex = info_out["female"].item()
+    if sex < 0.5:
+        sex = "male"
+    else:
+        sex = "female"
+    info_string += f"\nThe patient's predicted sex is {sex}."
+
+    if view in {0, 1}:
+        ctr = calculate_ctr(info_mask.cpu().numpy())
+        info_string += f"\nThe estimated cardiothoracic ratio (CTR) is {ctr:0.2f}."
+        if view == 0:
+            info_string += (
+                "\nNote that the cardiac silhuoette is magnified in the AP projection."
+            )
+
+    if view == 2:
+        info_string += (
+            "\nNOTE: The below outputs are NOT VALID for lateral radiographs."
+        )
+
+    x = pna_model.preprocess(Radiograph)
+    x = torch.from_numpy(x).float().to(device)
+    x = rearrange(x, "h w -> 1 1 h w")
+
+    with torch.inference_mode():
+        pna_out = pna_model(x)
+
+    pna_mask = pna_out["mask"]
+    h, w = rg.shape[:2]
+    pna_mask = F.interpolate(pna_mask, size=(h, w), mode="bilinear")
+    pna_mask = (pna_mask.cpu().numpy()[0, 0] * 255).astype(np.uint8)
+    pna_mask = cv2.applyColorMap(pna_mask, cv2.COLORMAP_JET)
+    pna_overlay = make_overlay(rg, pna_mask[..., ::-1])
+
+    x = ptx_model.preprocess(Radiograph)
+    x = torch.from_numpy(x).float().to(device)
+    x = rearrange(x, "h w -> 1 1 h w")
+
+    with torch.inference_mode():
+        ptx_out = ptx_model(x)
+
+    ptx_mask = ptx_out["mask"]
+    h, w = rg.shape[:2]
+    ptx_mask = F.interpolate(ptx_mask, size=(h, w), mode="bilinear")
+    ptx_mask = (ptx_mask.cpu().numpy()[0, 0] * 255).astype(np.uint8)
+    ptx_mask = cv2.applyColorMap(ptx_mask, cv2.COLORMAP_JET)
+    ptx_overlay = make_overlay(rg, ptx_mask[..., ::-1])
+
+    preds = {"Pneumonia": pna_out["cls"].item(), "Pneumothorax": ptx_out["cls"].item()}
+    return [info_string, preds, info_overlay, pna_overlay, ptx_overlay]
+
+
+image = gr.Image(image_mode="L")
+info_textbox = gr.Textbox(show_label=False)
+labels = gr.Label(show_label=False, show_heading=False)
+heatmap1 = gr.Image(image_mode="RGB", label="Heart & Lungs")
+heatmap2 = gr.Image(image_mode="RGB", label="Pneumonia")
+heatmap3 = gr.Image(image_mode="RGB", label="Pneumothorax")
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+    # Deep Learning for Chest Radiographs
+
+    This demo uses 3 models for chest radiographs:
+    1) Heart and lungs segmentation, with age, view, and sex prediction <https://huggingface.co/ianpan/chest-x-ray-basic>
+    2) Pneumonia classification and segmentation <https://huggingface.co/ianpan/pneumonia-cxr>
+    3) Pneumothorax classification and segmentation <https://huggingface.co/ianpan/pneumothorax-cxr>
+
+    Note that the pneumonia and pneumothorax heatmaps produced by this model are based on pixel-level segmentation maps.
+    Thus, they are expected to be more accurate than non-explicit localization methods such as GradCAM.
+
+    The example radiograph is my own, from when I had pneumonia. 
+
+    This model is for demonstration purposes only and has NOT been approved by any regulatory agency for clinical use. The user assumes
+    any and all responsibility regarding their own use of this model and its outputs. Do NOT upload any images containing protected
+    health information, as this demonstration is not compliant with patient privacy laws.
+
+    Created by: Ian Pan, <https://ianpan.me>
+    
+    Last updated: December 27, 2024
+    """
+    )
+    gr.Interface(
+        fn=predict,
+        inputs=image,
+        outputs=[info_textbox, labels, heatmap1, heatmap2, heatmap3],
+        examples=["examples/cxr.png"],
+        cache_examples=True,
+    )
+
+if __name__ == "__main__":
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    print(f"Using device `{device}` ...")
+
+    cxr_info_model = (
+        AutoModel.from_pretrained("ianpan/chest-x-ray-basic", trust_remote_code=True)
+        .eval()
+        .to(device)
+    )
+    pna_model = (
+        AutoModel.from_pretrained("ianpan/pneumonia-cxr", trust_remote_code=True)
+        .eval()
+        .to(device)
+    )
+    ptx_model = (
+        AutoModel.from_pretrained("ianpan/pneumothorax-cxr", trust_remote_code=True)
+        .eval()
+        .to(device)
+    )
+
+    demo.launch(share=True)

requirements.txt

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+albumentations
+einops
+gradio
+scikit-image
+spaces
+timm
+torch
+transformers