Dr. Tongue: Sign-Oriented Multi-label Detection for Remote Tongue Diagnosis
Authors: Yiliang Chen, Steven SC Ho, Cheng Xu, Yao Jie Xie, Wing-Fai Yeung, Shengfeng He, Jing Qin
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate our methodology, we developed an extensive tongue image dataset specifically designed for telemedicine. Unlike existing datasets, ours is tailored for remote diagnosis, with a comprehensive set of attribute labels. This dataset will be openly available, providing a valuable resource for research. Initial tests have shown improved accuracy in detecting various tongue attributes, highlighting our frameworkâs potential as an essential tool for remote medical assessments. |
| Researcher Affiliation | Academia | 1School of Nursing, The Hong Kong Polytechnic University, Hong Kong 2Singapore Management University, Singapore EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Tongue Image Upright Orientation Process |
| Open Source Code | Yes | Resources https://github.com/tonguedx/tonguedx. |
| Open Datasets | Yes | To validate our methodology, we developed an extensive tongue image dataset specifically designed for telemedicine. ... This dataset will be openly available, providing a valuable resource for research. |
| Dataset Splits | Yes | Quantitative comparison among different methods using five-fold cross-validation (values in mean% std%). |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments, such as specific GPU models, CPU models, or memory details. |
| Software Dependencies | No | The paper mentions several software components like Grounding DINO, SAM, YOLOv5-Mobile Net V3, Mobile SAM, ResNet50, ViT, and Trans FG, but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | No | The paper describes the loss function (Ltotal = wcolor Lcolor + wfur Lfur + Lattr) and mentions sigmoid cross-entropy losses and frequency-based weights. However, it does not provide specific numerical values for hyperparameters such as learning rates, batch sizes, number of epochs, or optimizer settings, which are crucial for a reproducible experimental setup. |