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 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## 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.
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-### Model Sources [optional]
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-## 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. -->
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-### Downstream Use [optional]
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-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.
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-## Training Details
-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
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-## Evaluation
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-### Testing Data, Factors & Metrics
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-#### Factors
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-#### Metrics
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## 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).
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-## Technical Specifications [optional]
-### Model Architecture and Objective
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-## Citation [optional]
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-## Glossary [optional]
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 ---
 library_name: transformers
+tags:
+- chest_x_ray
+- x_ray
+- medical_imaging
+- radiology
+- segmentation
+- classification
+- lungs
+- heart
+base_model:
+- timm/tf_efficientnetv2_s.in21k_ft_in1k
+pipeline_tag: image-segmentation
 ---
+This model performs both segmentation and classification on chest radiographs (X-rays).
+For frontal radiographs, the model segments the: 1) right lung, 2) left lung, and 3) heart.
+The model also predicts the chest X-ray view (AP, PA, lateral), patient age, and patient sex.
+The [CheXpert](https://stanfordmlgroup.github.io/competitions/chexpert/) (small version) and [NIH Chest X-ray](https://nihcc.app.box.com/v/ChestXray-NIHCC) datasets were used to train the model.
+Segmentation masks were obtained from the CheXmask [dataset](https://physionet.org/content/chexmask-cxr-segmentation-data/0.4/) ([paper](https://www.nature.com/articles/s41597-024-03358-1)).
+The final dataset comprised 335,516 images from 96,385 patients and was split into 80% training/20% validation. A holdout test set was not used since minimal tuning was performed.
+Validation performance as follows:
+```
+Segmentation (Dice similary coefficient):
+  Right Lung: 0.853
+   Left Lung: 0.844
+       Heart: 0.839
+Age Prediction:
+  Mean Absolute Error: 5.42 years
+Classification (AUC):
+  View:
+         AP: 0.999
+         PA: 0.998
+    Lateral: 1.000
+  Female: 0.999
+```
+To use the model:
+```
+import cv2
+import torch
+from transformers import AutoModel
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = AutoModel.from_pretrained("ianpan/chest-x-ray-basic", trust_remote_code=True)
+model = model.eval().to(device)
+img = cv2.imread(..., 0)
+x = model.preprocess(img) # only takes single image as input
+x = torch.from_numpy(x).unsqueeze(0).unsqueeze(0) # add channel, batch dims
+x = x.float()
+with torch.inference_mode():
+  out = model(x.to(device))
+```
+The output is a dictionary which contains 4 keys:
+* `mask` has 3 channels containing the segmentation masks. Take the argmax over the channel dimension to create a single image mask (i.e., `out["mask"].argmax(1)`): 1 = right lung, 2 = left lung, 3 = heart.
+* `age`, in years.
+* `view`, with 3 classes for each possible view. Take the argmax to select the predicted view (i.e., `out["view"].argmax(1)`): 0 = AP, 1 = PA, 2 = lateral.
+* `female`, binarize with `out["female"] >= 0.5`.
+You can use the segmentation mask to crop the region containing the lungs from the rest of the X-ray.
+You can also calculate the [cardiothoracic ratio (CTR)](https://radiopaedia.org/articles/cardiothoracic-ratio?lang=us) using this function:
+```
+import numpy as np
+def calculate_ctr(mask): # single mask with dims (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
+```
+If you have `pydicom` installed, you can also load a DICOM image directly:
+```
+img = model.load_image_from_dicom(path_to_dicom)
+```
+This model is for demonstration and research 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.