ianpan
/

mammo-crop

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