How to use image volumes with no boxes in the training dataset for RetinaNet 3D?

Hi, I am training a model for detecting blood clots in an abdominal artery on CT using the RetinaNet 3D model. To do this, I need to include image volumes with no clots - otherwise the model will just draw a box around that artery each time, regardless of whether it has a clot or not. Is it possible to train on image volumes that have no boxes? I tried to pass in json objects for these studies with an empty list for box coordinates, like so:

    {
        "image": "1.2.392.200036.9116.2.6.120663787.309230_neg.nii.gz",
        "box": [],
        "label": []
    },

But this throws the following error:

epoch 1/300
Traceback (most recent call last):
File "detection/luna16_training.py", line 476, in
main()
File "detection/luna16_training.py", line 278, in main
for batch_data in train_loader:
File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 628, in next
data = self._next_data()
File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1333, in _next_data
return self._process_data(data)
File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1359, in _process_data
data.reraise()
File "/opt/conda/lib/python3.8/site-packages/torch/_utils.py", line 543, in reraise
raise exception
RuntimeError: Caught RuntimeError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/opt/conda/lib/python3.8/site-packages/monai/transforms/transform.py", line 102, in apply_transform
return _apply_transform(transform, data, unpack_items)
File "/opt/conda/lib/python3.8/site-packages/monai/transforms/transform.py", line 66, in _apply_transform
return transform(parameters)
File "/opt/conda/lib/python3.8/site-packages/monai/apps/detection/transforms/dictionary.py", line 188, in call
d[key] = self.converter(d[key])
File "/opt/conda/lib/python3.8/site-packages/monai/apps/detection/transforms/array.py", line 166, in call
return convert_box_to_standard_mode(boxes, mode=self.mode)
File "/opt/conda/lib/python3.8/site-packages/monai/data/box_utils.py", line 576, in convert_box_to_standard_mode
return convert_box_mode(boxes=boxes, src_mode=mode, dst_mode=StandardMode())
File "/opt/conda/lib/python3.8/site-packages/monai/data/box_utils.py", line 535, in convert_box_mode
corners = src_boxmode.boxes_to_corners(boxes_t)
File "/opt/conda/lib/python3.8/site-packages/monai/data/box_utils.py", line 308, in boxes_to_corners
spatial_dims = get_spatial_dims(boxes=boxes)
File "/opt/conda/lib/python3.8/site-packages/monai/data/box_utils.py", line 396, in get_spatial_dims
if int(boxes.shape[1]) not in [4, 6]:
IndexError: tuple index out of range

I am not surprised by the error since the code appears to assume there will always be at least 1 box. Is there any current way in the monai code to handle image volumes without boxes?

Thanks!

Traccia i dati delle box vuote attraverso detection/luna16_training.py e le transforms in monai/apps/detection/transforms/dictionary.py e array.py, quindi esamina monai/data/box_utils.py, dove si verifica l’IndexError segnalato. Determina se i volumi di immagini con liste vuote di box e label possono passare attraverso la pipeline esistente e verifica che il training accetti tali sample senza questo errore.