# Automatic construction of Chinese herbal prescription from tongue image via CNNs and auxiliary latent therapy topics

arXiv: https://arxiv.org/abs/1802.02203 · HTML: https://mlsanity.com/paper/1802.02203/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2018.

## Built on by (most cited first, 5 of 5)

- [A optimization framework for herbal prescription planning based on deep reinforcement learning](https://mlsanity.com/paper/2304.12828/) (2023)
- [Tianyi: A Traditional Chinese Medicine all-rounder language model and its Real-World Clinical Practice](https://mlsanity.com/paper/2505.13156/) (2025)
- [Securing AI-based Healthcare Systems using Blockchain Technology: A State-of-the-Art Systematic Literature Review and Future Research Directions](https://mlsanity.com/paper/2206.04793/) (2022)
- [Dr. Tongue: Sign-Oriented Multi-label Detection for Remote Tongue Diagnosis](https://mlsanity.com/paper/2501.03053/) (2025)
- [From Physician Expertise to Clinical Agents: Preserving, Standardizing, and Scaling Physicians' Medical Expertise with Lightweight LLM](https://mlsanity.com/paper/2603.23520/) (2026)

## Abstract (arXiv)

The tongue image provides important physical information of humans. It is of great importance for diagnoses and treatments in clinical medicine. Herbal prescriptions are simple, noninvasive and have low side effects. Thus, they are widely applied in China. Studies on the automatic construction technology of herbal prescriptions based on tongue images have great significance for deep learning to explore the relevance of tongue images for herbal prescriptions, it can be applied to healthcare services in mobile medical systems. In order to adapt to the tongue image in a variety of photographic environments and construct herbal prescriptions, a neural network framework for prescription construction is designed. It includes single/double convolution channels and fully connected layers. Furthermore, it proposes the auxiliary therapy topic loss mechanism to model the therapy of Chinese doctors and alleviate the interference of sparse output labels on the diversity of results. The experiment use the real world tongue images and the corresponding prescriptions and the results can generate prescriptions that are close to the real samples, which verifies the feasibility of the proposed method for the automatic construction of herbal prescriptions from tongue images. Also, it provides a reference for automatic herbal prescription construction from more physical information.