We lifted 13 functions out of this paper's own repositories and ran 10 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| scott-yjyang/vivim | canonical | 10 of 13 |
| Function | Status | Where it lives |
|---|---|---|
| binary_entropy_loss | Ran | scott-yjyang/vivim/loss.py pointer only (licence: NOASSERTION) · get_code("c6e70d5ac2de73bf") |
| causal_conv1d_ref | Ran | scott-yjyang/vivim/causal-conv1d/causal_conv1d/causal_conv1d_interface.py pointer only (licence: NONE) · get_code("0595d8b8521fd9ca") |
| cv_random_flip | Ran | scott-yjyang/vivim/data_polyp.py pointer only (licence: NONE) · get_code("41839b27773cccf7") |
| jaccard | Ran | scott-yjyang/vivim/misc2.py pointer only (licence: NOASSERTION) · get_code("00364c3c6b4b23e8") |
| load_model_from_dict | Ran | scott-yjyang/vivim/modeling/utils.py pointer only (licence: NOASSERTION) · get_code("418a04607e943f31") |
| mask_iou | Ran | scott-yjyang/vivim/loss.py pointer only (licence: NOASSERTION) · get_code("53bd64a4a61b8042") |
| randomCrop | Ran | scott-yjyang/vivim/data_polyp.py pointer only (licence: NONE) · get_code("b8fc70f2a16b23cc") |
| randomRotation | Ran | scott-yjyang/vivim/data_polyp.py pointer only (licence: NONE) · get_code("8d40bcc4f2cf3edb") |
| structure_loss | Ran | scott-yjyang/vivim/train_pl_polyp.py pointer only (licence: NONE) · get_code("797eafbb72dabcfd") |
| structure_loss | Ran | scott-yjyang/vivim/modeling/utils.py pointer only (licence: NOASSERTION) · get_code("a0c651abef32e608") |
| cross_entropy_loss | Not yet run | scott-yjyang/vivim/loss.py pointer only (licence: NOASSERTION) · get_code("e4f26582292cf067") |
| dice | Not yet run | scott-yjyang/vivim/misc2.py pointer only (licence: NOASSERTION) · get_code("1b2b6e788bbbb51a") |
| precision | Not yet run | scott-yjyang/vivim/misc2.py pointer only (licence: NOASSERTION) · get_code("a0be218082173339") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Medical video segmentation gains increasing attention in clinical practice due to the redundant dynamic references in video frames. However, traditional convolutional neural networks have a limited receptive field and transformer-based networks are mediocre in constructing long-term dependency from the perspective of computational complexity. This bottleneck poses a significant challenge when processing longer sequences in medical video analysis tasks using available devices with limited memory. Recently, state space models (SSMs), famous by Mamba, have exhibited impressive achievements in efficient long sequence modeling, which develops deep neural networks by expanding the receptive field on many vision tasks significantly. Unfortunately, vanilla SSMs failed to simultaneously capture causal temporal cues and preserve non-casual spatial information. To this end, this paper presents a Video Vision Mamba-based framework, dubbed as Vivim, for medical video segmentation tasks. Our Vivim can effectively compress the long-term spatiotemporal representation into sequences at varying scales with our designed Temporal Mamba Block. We also introduce an improved boundary-aware affine constraint across frames to enhance the discriminative ability of Vivim on ambiguous lesions. Extensive experiments on thyroid segmentation, breast lesion segmentation in ultrasound videos, and polyp segmentation in colonoscopy videos demonstrate the effectiveness and efficiency of our Vivim, superior to existing methods. The code is available at: https://github.com/scott-yjyang/Vivim. The dataset will be released once accepted.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2401.14168")
get_code_for_paper("2401.14168")
have("2401.14168")
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