Gallbladder cancer detection via ultrasound image analysis: An end‐to‐end hierarchical feature‐fused model

Abstract Gallbladder cancer is a fatal disease, and its early diagnosis can significantly impact patient treatment. Ultrasound imaging is often the initial diagnostic test for gallbladder cancer, making the enhancement of cancer detection accuracy from these images crucial. Despite the promising results of artificial intelligence techniques in disease diagnosis, their black‐box nature hinders the reliability of their results and their practical application. Therefore, it is essential not to rely solely on a single model's output and to further investigate for more reliable outcomes. This study presents a step‐by‐step structural investigation of forming an end‐to‐end model, a conjunction of two convolutional neural network based methods, for detecting gallbladder conditions. The final model, leveraging feature fusions and hierarchical classification, achieved a high accuracy of 92.62% for detecting normal, benign, and malignant gallbladders. It also achieved a remarkable accuracy of 98.36% for classifying normal and non‐normal instances and 92.22% for classifying benign and malignant cases. Finally, comprehensive post‐processing investigations, including cross‐validation, temperature scaling, and uncertainty estimation, along with error analysis, are conducted to gain more insights into the model's output. Among these insights, the model demonstrated resilience of its results to active dropout and augmentation at the inference phase. Furthermore, when applied with test‐time data augmentation, uncertainty estimation methods have better distinguishability between the uncertainties of correctly and incorrectly classified instances, which provides additional information about the model's output. The source code of experiments conducted in this study is available at https://github.com/SaraDadjouy/GBCRet .