基于Chinese-LLaMA-Alpaca-3的多模态中医舌诊辅助诊断系统设计与实现

发布于：2025-07-21 ⋅ 阅读:(16) ⋅ 点赞:(0)

基于Chinese-LLaMA-Alpaca-3的多模态中医舌诊辅助诊断系统设计与实现

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摘要

本文详细阐述了开发一个基于Chinese-LLaMA-Alpaca-3的多模态中医专病大模型的全过程，该系统专注于舌诊辅助诊断，支持文本、语音和图像输入，并构建完整的Neo4j知识图谱。系统整合了现代深度学习技术与传统中医理论，通过多模态数据处理、知识图谱构建和智能推理引擎，为中医诊断提供智能化辅助工具。

关键词：中医舌诊、多模态大模型、知识图谱、Chinese-LLaMA-Alpaca-3、Neo4j

1. 引言

中医舌诊作为"望诊"的重要组成部分，具有悠久的历史和丰富的理论体系。传统舌诊主要依赖医师的经验和主观判断，存在标准化程度低、经验传承困难等问题。随着人工智能技术的发展，特别是大语言模型和多模态技术的进步，为中医舌诊的智能化辅助诊断提供了新的可能性。

本项目旨在开发一个基于Chinese-LLaMA-Alpaca-3的多模态中医专病大模型，通过整合文本、语音和图像等多种输入方式，结合Neo4j构建的完整中医舌诊知识图谱，实现舌诊的智能化辅助诊断，提高诊断的准确性和一致性，同时为中医知识的传承和创新提供技术支持。

2. 系统架构设计

2.1 整体架构

系统采用模块化设计，主要分为以下几个核心模块：

  1. 多模态输入处理模块：负责处理文本、语音和图像输入
  2. Chinese-LLaMA-Alpaca-3核心模型：进行多模态数据理解和推理
  3. Neo4j知识图谱模块：存储和查询中医舌诊知识
  4. 诊断推理引擎：结合大模型和知识图谱进行诊断推理
  5. 输出模块：生成诊断报告和解释

2.2 技术栈选择

  • 大语言模型：Chinese-LLaMA-Alpaca-3（中文优化版LLaMA模型）
  • 知识图谱：Neo4j图数据库
  • 图像处理：OpenCV、PyTorch Vision
  • 语音处理：Whisper语音识别模型
  • 后端框架：FastAPI
  • 前端框架：Vue.js（可选）
  • 部署工具：Docker、Kubernetes

3. 多模态输入处理模块实现

3.1 文本输入处理

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

class TextProcessor:
    def __init__(self, model_path="Chinese-LLaMA-Alpaca-3"):
        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
        self.model = AutoModelForCausalLM.from_pretrained(model_path)
        
    def process(self, text):
        inputs = self.tokenizer(text, return_tensors="pt")
        with torch.no_grad():
            outputs = self.model.generate(**inputs, max_length=512)
        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)

3.2 语音输入处理

import whisper
from pydub import AudioSegment

class AudioProcessor:
    def __init__(self, model_size="medium"):
        self.model = whisper.load_model(model_size)
        
    def process(self, audio_path):
        # 支持多种音频格式转换
        if not audio_path.endswith('.wav'):
            audio = AudioSegment.from_file(audio_path)
            audio_path = audio_path.split('.')[0] + '.wav'
            audio.export(audio_path, format='wav')
            
        result = self.model.transcribe(audio_path)
        return result['text']

3.3 图像输入处理

import cv2
import numpy as np
from PIL import Image
from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer

class ImageProcessor:
    def __init__(self):
        self.feature_extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
        self.model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
        self.tokenizer = AutoTokenizer.from_pretrained("gpt2")
        
    def process(self, image_path):
        image = Image.open(image_path)
        if image.mode != "RGB":
            image = image.convert(mode="RGB")
            
        # 舌体分割和特征提取
        tongue_mask = self._segment_tongue(image)
        tongue_features = self._extract_tongue_features(image, tongue_mask)
        
        # 图像描述生成
        pixel_values = self.feature_extractor(images=image, return_tensors="pt").pixel_values
        output_ids = self.model.generate(pixel_values, max_length=128, num_beams=4)
        caption = self.tokenizer.decode(output_ids[0], skip_special_tokens=True)
        
        return {
            "caption": caption,
            "features": tongue_features,
            "mask": tongue_mask
        }
    
    def _segment_tongue(self, image):
        # 使用深度学习模型进行舌体分割
        # 实现细节省略...
        pass
    
    def _extract_tongue_features(self, image, mask):
        # 提取舌体颜色、纹理、形状等特征
        # 实现细节省略...
        pass

4. Chinese-LLaMA-Alpaca-3核心模型集成

4.1 模型加载与初始化

from transformers import LlamaForCausalLM, LlamaTokenizer
import torch

class TCMLLaMAModel:
    def __init__(self, model_path, device="cuda" if torch.cuda.is_available() else "cpu"):
        self.device = device
        self.tokenizer = LlamaTokenizer.from_pretrained(model_path)
        self.model = LlamaForCausalLM.from_pretrained(model_path).to(device)
        
        # 加载中医舌诊特定LoRA适配器
        self._load_tcm_lora()
        
    def _load_tcm_lora(self):
        # 加载针对中医舌诊微调的LoRA权重
        # 实现细节省略...
        pass
    
    def generate_response(self, prompt, max_length=512, temperature=0.7):
        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
        
        with torch.no_grad():
            outputs = self.model.generate(
                **inputs,
                max_length=max_length,
                temperature=temperature,
                top_p=0.9,
                do_sample=True,
                pad_token_id=self.tokenizer.eos_token_id
            )
            
        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)

4.2 多模态数据融合

class MultimodalFusion:
    def __init__(self, text_model, image_processor):
        self.text_model = text_model
        self.image_processor = image_processor
        
    def fuse_modalities(self, text_input=None, image_input=None, audio_input=None):
        # 处理文本输入
        text_features = self._process_text(text_input) if text_input else None
        
        # 处理图像输入
        image_features = self._process_image(image_input) if image_input else None
        
        # 处理语音输入（先转换为文本）
        audio_text = self._process_audio(audio_input) if audio_input else None
        audio_features = self._process_text(audio_text) if audio_text else None
        
        # 多模态特征融合
        fused_features = self._fuse_features(text_features, image_features, audio_features)
        
        return fused_features
    
    def _process_text(self, text):
        # 文本特征提取
        pass
    
    def _process_image(self, image):
        # 图像特征提取
        image_data = self.image_processor.process(image)
        return image_data
    
    def _process_audio(self, audio):
        # 语音特征提取（通过语音识别转换为文本后处理）
        pass
    
    def _fuse_features(self, *features):
        # 多模态特征融合策略
        pass

5. Neo4j知识图谱构建

5.1 知识图谱设计

中医舌诊知识图谱主要包括以下实体和关系：

  • 实体类型：

    • 舌象（颜色、形状、苔质等）
    • 证型（阴虚、阳虚、气虚等）
    • 症状
    • 治法
    • 方剂
    • 药材

  • 关系类型：

    • 舌象-指示-证型
    • 证型-关联-症状
    • 证型-对应-治法
    • 治法-包含-方剂
    • 方剂-组成-药材

5.2 Neo4j数据库操作

from py2neo import Graph, Node, Relationship

class TCMKnowledgeGraph:
    def __init__(self, uri, user, password):
        self.graph = Graph(uri, auth=(user, password))
        
    def create_tongue_node(self, tongue_properties):
        tongue = Node("Tongue", **tongue_properties)
        self.graph.create(tongue)
        return tongue
    
    def create_syndrome_node(self, syndrome_properties):
        syndrome = Node("Syndrome", **syndrome_properties)
        self.graph.create(syndrome)
        return syndrome
    
    def create_indicates_relationship(self, tongue_node, syndrome_node, confidence):
        indicates = Relationship(tongue_node, "INDICATES", syndrome_node, confidence=confidence)
        self.graph.create(indicates)
        return indicates
    
    def query_related_syndromes(self, tongue_description):
        query = """
        MATCH (t:Tongue {description: $desc})-[:INDICATES]->(s:Syndrome)
        RETURN s.name AS syndrome, s.description AS description, s.treatment AS treatment
        ORDER BY s.probability DESC
        LIMIT 5
        """
        return self.graph.run(query, desc=tongue_description).data()
    
    def build_initial_knowledge_graph(self):
        # 从中医古籍和现代文献中构建初始知识图谱
        # 实现细节省略...
        pass

5.3 知识图谱与大模型协同

class KGEnhancedDiagnosis:
    def __init__(self, llm_model, kg_connector):
        self.llm = llm_model
        self.kg = kg_connector
        
    def diagnose(self, tongue_description):
        # 第一步：从知识图谱中检索相关证型
        kg_results = self.kg.query_related_syndromes(tongue_description)
        
        # 第二步：使用大模型对结果进行精炼和解释
        prompt = self._build_diagnosis_prompt(tongue_description, kg_results)
        diagnosis = self.llm.generate_response(prompt)
        
        return {
            "raw_kg_results": kg_results,
            "refined_diagnosis": diagnosis
        }
    
    def _build_diagnosis_prompt(self, tongue_desc, kg_results):
        prompt = """你是一位经验丰富的中医专家，请根据以下舌象描述和初步知识图谱检索结果，给出综合诊断意见：
        
舌象描述：{}
        
知识图谱检索结果：{}
        
请综合分析舌象特征与可能的证型关联，考虑患者的整体情况，给出最可能的3个证型诊断，并分别说明诊断依据和推荐的治疗方案。""".format(tongue_desc, str(kg_results))
        
        return prompt

6. 诊断推理引擎实现

6.1 多模态诊断流程

class DiagnosisEngine:
    def __init__(self, text_processor, audio_processor, image_processor, llm_model, kg):
        self.text_proc = text_processor
        self.audio_proc = audio_processor
        self.image_proc = image_processor
        self.llm = llm_model
        self.kg = kg
        self.fusion = MultimodalFusion(llm_model, image_processor)
        
    def process_input(self, text=None, audio=None, image=None):
        # 多模态数据处理
        fused_features = self.fusion.fuse_modalities(text, image, audio)
        
        # 舌象描述生成
        tongue_description = self._generate_tongue_description(fused_features)
        
        # 知识图谱增强诊断
        diagnosis = self.kg_enhanced_diagnosis(tongue_description)
        
        # 生成完整诊断报告
        report = self._generate_report(tongue_description, diagnosis)
        
        return report
    
    def _generate_tongue_description(self, features):
        prompt = """根据以下舌象特征生成专业的中医舌象描述：
        
颜色：{}
苔质：{}
形状：{}
其他特征：{}
        
请用标准的中医术语描述舌象，包括舌质和舌苔的特征。""".format(
            features.get('color', '未知'),
            features.get('coating', '未知'),
            features.get('shape', '未知'),
            features.get('other', '无')
        )
        
        return self.llm.generate_response(prompt)
    
    def _generate_report(self, tongue_desc, diagnosis):
        prompt = """根据以下舌象描述和诊断结果，生成一份完整的中医舌诊报告：
        
舌象描述：{}
        
诊断结果：{}
        
报告应包括以下部分：
1. 舌象特征总结
2. 证型分析
3. 病因病机解释
4. 治疗建议（包括方剂推荐）
5. 生活调养建议""".format(tongue_desc, str(diagnosis))
        
        return self.llm.generate_response(prompt, max_length=1024)

6.2 反馈学习机制

class FeedbackLearner:
    def __init__(self, kg):
        self.kg = kg
        
    def add_expert_feedback(self, tongue_description, correct_syndromes, incorrect_syndromes):
        # 强化正确关联
        for syndrome in correct_syndromes:
            self._strengthen_relationship(tongue_description, syndrome)
            
        # 弱化或移除错误关联
        for syndrome in incorrect_syndromes:
            self._weaken_relationship(tongue_description, syndrome)
            
        # 添加新的知识节点（如有）
        self._add_new_knowledge(tongue_description, correct_syndromes)
    
    def _strengthen_relationship(self, tongue_desc, syndrome):
        query = """
        MATCH (t:Tongue {description: $desc})-[r:INDICATES]->(s:Syndrome {name: $name})
        SET r.confidence = coalesce(r.confidence, 0.5) + 0.1
        RETURN r
        """
        self.kg.graph.run(query, desc=tongue_desc, name=syndrome)
    
    def _weaken_relationship(self, tongue_desc, syndrome):
        query = """
        MATCH (t:Tongue {description: $desc})-[r:INDICATES]->(s:Syndrome {name: $name})
        SET r.confidence = coalesce(r.confidence, 0.5) - 0.1
        WITH r WHERE r.confidence <= 0.2
        DELETE r
        """
        self.kg.graph.run(query, desc=tongue_desc, name=syndrome)
    
    def _add_new_knowledge(self, tongue_desc, syndromes):
        # 检查舌象节点是否存在
        tongue_query = """
        MERGE (t:Tongue {description: $desc})
        RETURN t
        """
        self.kg.graph.run(tongue_query, desc=tongue_desc)
        
        # 添加新的证型关联
        for syndrome in syndromes:
            syndrome_query = """
            MERGE (s:Syndrome {name: $name})
            MERGE (t:Tongue {description: $desc})
            MERGE (t)-[r:INDICATES {confidence: 0.7}]->(s)
            RETURN r
            """
            self.kg.graph.run(syndrome_query, name=syndrome, desc=tongue_desc)

7. 系统部署与API设计

7.1 FastAPI后端实现

from fastapi import FastAPI, UploadFile, File, Form
from fastapi.middleware.cors import CORSMiddleware
import tempfile
import os

app = FastAPI(title="中医舌诊辅助诊断系统")

# 允许跨域请求
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# 初始化各组件
text_processor = TextProcessor()
audio_processor = AudioProcessor()
image_processor = ImageProcessor()
llm_model = TCMLLaMAModel("Chinese-LLaMA-Alpaca-3")
kg = TCMKnowledgeGraph("bolt://localhost:7687", "neo4j", "password")
diagnosis_engine = DiagnosisEngine(text_processor, audio_processor, image_processor, llm_model, kg)

@app.post("/diagnose/text")
async def diagnose_from_text(description: str = Form(...)):
    try:
        result = diagnosis_engine.process_input(text=description)
        return {"status": "success", "result": result}
    except Exception as e:
        return {"status": "error", "message": str(e)}

@app.post("/diagnose/audio")
async def diagnose_from_audio(audio: UploadFile = File(...)):
    try:
        # 保存临时音频文件
        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
            content = await audio.read()
            tmp.write(content)
            tmp_path = tmp.name
            
        # 处理音频
        result = diagnosis_engine.process_input(audio=tmp_path)
        
        # 删除临时文件
        os.unlink(tmp_path)
        
        return {"status": "success", "result": result}
    except Exception as e:
        if 'tmp_path' in locals() and os.path.exists(tmp_path):
            os.unlink(tmp_path)
        return {"status": "error", "message": str(e)}

@app.post("/diagnose/image")
async def diagnose_from_image(image: UploadFile = File(...)):
    try:
        # 保存临时图像文件
        with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp:
            content = await image.read()
            tmp.write(content)
            tmp_path = tmp.name
            
        # 处理图像
        result = diagnosis_engine.process_input(image=tmp_path)
        
        # 删除临时文件
        os.unlink(tmp_path)
        
        return {"status": "success", "result": result}
    except Exception as e:
        if 'tmp_path' in locals() and os.path.exists(tmp_path):
            os.unlink(tmp_path)
        return {"status": "error", "message": str(e)}

@app.post("/feedback")
async def provide_feedback(
    tongue_description: str = Form(...),
    correct_syndromes: List[str] = Form([]),
    incorrect_syndromes: List[str] = Form([])
):
    try:
        learner = FeedbackLearner(kg)
        learner.add_expert_feedback(tongue_description, correct_syndromes, incorrect_syndromes)
        return {"status": "success"}
    except Exception as e:
        return {"status": "error", "message": str(e)}

7.2 Docker部署配置

# 基础镜像
FROM python:3.9-slim

# 设置工作目录
WORKDIR /app

# 安装系统依赖
RUN apt-get update && apt-get install -y \
    ffmpeg \
    libsm6 \
    libxext6 \
    && rm -rf /var/lib/apt/lists/*

# 复制requirements文件并安装Python依赖
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# 复制应用代码
COPY . .

# 暴露端口
EXPOSE 8000

# 启动命令
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

8. 系统评估与优化

8.1 评估指标

  1. 诊断准确率：与资深中医专家诊断结果对比
  2. 响应时间：各模块处理时间
  3. 用户满意度：通过问卷调查获取
  4. 知识图谱覆盖率：覆盖的舌象-证型关联比例
  5. 模型一致性：对相同舌象多次诊断的结果一致性

8.2 优化策略

  1. 模型微调：使用更多中医舌诊数据对Chinese-LLaMA-Alpaca-3进行领域适配
  2. 知识图谱扩充：持续从古籍和现代文献中提取知识
  3. 缓存机制：对常见舌象诊断结果进行缓存
  4. 并行处理：多模态数据并行处理提高效率
  5. 量化部署：使用模型量化技术减少资源占用

9. 结论与展望

本文详细介绍了基于Chinese-LLaMA-Alpaca-3的多模态中医舌诊辅助诊断系统的设计与实现。系统通过整合多模态输入处理、大语言模型推理和知识图谱技术，实现了较为准确和可解释的中医舌诊辅助诊断。Neo4j知识图谱的引入不仅提高了诊断的可信度，也为中医知识的系统化整理和传承提供了有效工具。

未来工作可以从以下几个方面展开：

  1. 多模态数据融合的进一步优化：探索更有效的跨模态特征融合方法
  2. 动态知识图谱更新：实现从临床数据中自动发现新知识
  3. 个性化诊断：结合患者病史和体质特征进行个性化分析
  4. 临床验证：开展大规模临床验证以评估系统实际效果
  5. 移动端优化：开发轻量化版本以适应移动端部署

中医智能化是一项长期而复杂的工程，需要医学专家与AI研究人员的紧密合作。本系统作为这一方向的探索，希望能为中医现代化发展提供有益参考。

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