# An Improved Heteroscedastic Modeling Method for Chest X-ray Image Classification with Noisy Labels
## 3 Methodology
### The Proposed GMM-HM
#### 3.2.4 Optimization
All authors have read and agreed to the published version of the manuscript. **Funding:** The research was funded by China Postdoctoral Science Foundation (2022M710338) and the Fundamental Research Funds for the Central Universities (2022 JBMC008). **Data Availability Statement:** The data used in this article are publicly available at ChestX-ray2017 ([[https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia](https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia)]([https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia](https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia)), accessed on 1 March 2023) and ChestX-ray14 ([[https://nihcc.app.box.com/v/ChestXray-NIHCC](https://nihcc.app.box.com/v/ChestXray-NIHCC)]([https://nihcc.app.box.com/v/ChestXray-NIHCC](https://nihcc.app.box.com/v/ChestXray-NIHCC)), accessed on 1 March 2023). **Conflicts of Interest:** The authors declare no conflict of interest. Figure 3: (**a–c**) are the visualized t-SNE feature distributions of the Baseline, HM, and the proposed GMM-HM methods on the ChestXray14 dataset. ## References

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