Lei Ma, Rossella Arcucci, Jie Fu, Mi Zhang, César Quilodrán-Casas, Benyou Wang, Zhongwei Wan, Che Liu, Sibo Cheng
We lifted 4 functions out of this paper's own repositories and ran 2 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 |
|---|---|---|
| sustechbruce/med-unic | canonical | 1 of 3 |
| SUSTechBruce/Med-UniC | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| sigmoid | Ran | sustechbruce/med-unic/zero-shot/zero_shot.py code served (permissive licence) · get_code("1846a09dc0fdcb18") |
| Language_discriminator | Ran | SUSTechBruce/Med-UniC/finetune/downstream_tasks/mgca/models/mgca/med_unic_model.py code served (permissive licence) · get_code("74c4e370c4bf9d9a") |
| predict | Not yet run | sustechbruce/med-unic/zero-shot/zero_shot.py code served (permissive licence) · get_code("bc60f10026a470a1") |
| zeroshot_classifier | Not yet run | sustechbruce/med-unic/zero-shot/zero_shot.py code served (permissive licence) · get_code("436e80777234abe0") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The scarcity of data presents a critical obstacle to the efficacy of medical visionlanguage pre-training (VLP). A potential solution lies in the combination of datasets from various language communities. Nevertheless, the main challenge stems from the complexity of integrating diverse syntax and semantics, language-specific medical terminology, and culture-specific implicit knowledge. Therefore, one crucial aspect to consider is the presence of community bias caused by different languages. This paper presents a novel framework named Unifying Cross-Lingual Medical Vision-Language Pre-Training (Med-UniC), designed to integrate multimodal medical data from the two most prevalent languages, English and Spanish. Specifically, we propose Cross-lingual Text Alignment Regularization (CTR) to explicitly unify cross-lingual semantic representations of medical reports originating from diverse language communities. CTR is optimized through latent language disentanglement, rendering our optimization objective to not depend on negative samples, thereby significantly mitigating the bias from determining positive-negative sample pairs within analogous medical reports. Furthermore, it ensures that the cross-lingual representation is not biased toward any specific language community. Med-UniC reaches superior performance across 5 medical image tasks and 10 datasets encompassing over 30 diseases, offering a versatile framework for unifying multi-modal medical data within diverse linguistic communities. The experimental outcomes highlight the presence of community bias in cross-lingual VLP. Reducing this bias enhances the performance not only in vision-language tasks but also in uni-modal visual tasks. The source code has been released at https://github.com/SUSTechBruce/Med-UniC.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2305.19894")
get_code_for_paper("2305.19894")
have("2305.19894")
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