We lifted 4 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| longbai1006/surgical-vqlaplus | canonical | 4 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| accuracy | Ran | longbai1006/surgical-vqlaplus/utils.py code served (permissive licence) · get_code("53daad1d7b99fbdf") |
| calc_acc | Ran | longbai1006/surgical-vqlaplus/utils.py code served (permissive licence) · get_code("19988d5d6c4a4d7c") |
| calc_classwise_acc | Ran | longbai1006/surgical-vqlaplus/utils.py code served (permissive licence) · get_code("9fd07c1d7cd49c46") |
| make_mask | Ran | longbai1006/surgical-vqlaplus/models/CATViLEmbedding.py code served (permissive licence) · get_code("e766a0a3063c15d0") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Medical visual question answering (VQA) bridges the gap between visual information and clinical decision-making, enabling doctors to extract understanding from clinical images and videos. In particular, surgical VQA can enhance the interpretation of surgical data, aiding in accurate diagnoses, effective education, and clinical interventions. However, the inability of VQA models to visually indicate the regions of interest corresponding to the given questions results in incomplete comprehension of the surgical scene. To tackle this, we propose the surgical visual question localized-answering (VQLA) for precise and context-aware responses to specific queries regarding surgical images. Furthermore, to address the strong demand for safety in surgical scenarios and potential corruptions in image acquisition and transmission, we propose a novel approach called Calibrated Co-Attention Gated Vision-Language (C$^2$G-ViL) embedding to integrate and align multimodal information effectively. Additionally, we leverage the adversarial sample-based contrastive learning strategy to boost our performance and robustness. We also extend our EndoVis-18-VQLA and EndoVis-17-VQLA datasets to broaden the scope and application of our data. Extensive experiments on the aforementioned datasets demonstrate the remarkable performance and robustness of our solution. Our solution can effectively combat real-world image corruption. Thus, our proposed approach can serve as an effective tool for assisting surgical education, patient care, and enhancing surgical outcomes.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2408.04958")
get_code_for_paper("2408.04958")
have("2408.04958")
Connect an agent — have() is free.