We lifted 3 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| copy not recorded | — | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| kaiming_normal_init_weight | Ran | this paper's copy was not recorded; identical code first harvested from ziyangwang007/mamba-unet pointer only · get_code("13f59065a97aef7e") |
| patients_to_slices | Ran | this paper's copy was not recorded; identical code first harvested from ziyangwang007/mamba-unet pointer only · get_code("f53c4c3922632c64") |
| xavier_normal_init_weight | Ran | this paper's copy was not recorded; identical code first harvested from ziyangwang007/mamba-unet pointer only · get_code("b49bacf6c71fb02c") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Medical image segmentation is essential in diagnostics, treatment planning, and healthcare, with deep learning offering promising advancements. Notably, the convolutional neural network (CNN) excels in capturing local image features, whereas the Vision Transformer (ViT) adeptly models long-range dependencies through multi-head self-attention mechanisms. Despite their strengths, both the CNN and ViT face challenges in efficiently processing long-range dependencies in medical images, often requiring substantial computational resources. This issue, combined with the high cost and limited availability of expert annotations, poses significant obstacles to achieving precise segmentation. To address these challenges, this study introduces Semi-Mamba-UNet, which integrates a purely visual Mamba-based U-shaped encoder-decoder architecture with a conventional CNN-based UNet into a semi-supervised learning (SSL) framework. This innovative SSL approach leverages both networks to generate pseudo-labels and cross-supervise one another at the pixel level simultaneously, drawing inspiration from consistency regularisation techniques. Furthermore, we introduce a self-supervised pixel-level contrastive learning strategy that employs a pair of projectors to enhance the feature learning capabilities further, especially on unlabelled data. Semi-Mamba-UNet was comprehensively evaluated on two publicly available segmentation dataset and compared with seven other SSL frameworks with both CNN- or ViT-based UNet as the backbone network, highlighting the superior performance of the proposed method. The source code of Semi-Mamba-Unet, all baseline SSL frameworks, the CNN- and ViT-based networks, and the two corresponding datasets are made publicly accessible.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2402.07245")
get_code_for_paper("2402.07245")
have("2402.07245")
Connect an agent — have() is free.