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--- |
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library_name: transformers |
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tags: |
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- radiology |
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- mammo_crop |
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- mammography |
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- medical_imaging |
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license: apache-2.0 |
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base_model: |
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- timm/mobilenetv3_small_100.lamb_in1k |
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pipeline_tag: object-detection |
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--- |
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This model crops mammography images to eliminate unnecessary background. The model uses a lightweight `mobilenetv3_small_100` backbone and predicts normalized `xywh` coordinates. |
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The model was trained and validated using 54,706 screening mammography images from the [RSNA Screening Mammography Breast Cancer Detection](https://www.kaggle.com/competitions/rsna-breast-cancer-detection/) challenge using a 90%/10% split. |
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On single-fold validation, the model achieved mean absolute errors (normalized coordinates) of: |
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``` |
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x: 0.0032 |
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y: 0.0030 |
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w: 0.0054 |
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h: 0.0088 |
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``` |
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The ground-truth coordinates were generated using the following code: |
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``` |
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import cv2 |
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def crop_roi(img): |
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img = img[5:-5, 5:-5] |
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output = cv2.connectedComponentsWithStats((img > 10).astype("uint8")[:, :], 8, cv2.CV_32S) |
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stats = output[2] |
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idx = stats[1:, 4].argmax() + 1 |
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x1, y1, w, h = stats[idx][:4] |
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x1 = max(0, x1 - 5) |
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y1 = max(0, y1 - 5) |
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img_h, img_w = img.shape[:2] |
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return x1, y1, w, h) |
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``` |
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While not guaranteed to be foolproof, a cursory review of a sample of cropped images demonstrated excellent performance. |
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The model was trained with a large batch size (256) to mitigate noise. |
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To use the model: |
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``` |
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import cv2 |
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import torch |
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from transformers import AutoModel |
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model = AutoModel.from_pretrained("ianpan/mammo-crop", trust_remote_code=True) |
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model = model.eval() |
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img = cv2.imread(..., 0) |
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img_shape = torch.tensor([img.shape[:2]]) |
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x = model.preprocess(img) |
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x = torch.from_numpy(x).unsqueeze(0).unsqueeze(0) |
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x = x.float() |
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# if you do not provide img_shape |
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# model will return normalized coordinates |
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with torch.inference_mode(): |
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coords = model(x, img_shape) |
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# only 1 sample in batch |
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coords = coords[0].numpy() |
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x, y, w, h = coords |
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# coords already rescaled with img_shape |
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cropped_img = img[y: y + h, x: x + w] |
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``` |
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If you have `pydicom` installed, you can also load a DICOM image directly: |
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``` |
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img = model.load_image_from_dicom(path_to_dicom) |
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``` |
