# A Retrospective Systematic Study on Hierarchical Sparse Query Transformer-assisted Ultrasound Screening for Early Hepatocellular Carcinoma Source: [A Retrospective Systematic Study on Hierarchical Sparse Query Transformer-assisted Ultrasound Screening for Early Hepatocellular Carcinoma]() Collection index: [llms.txt]() Data updated: 2026-01-02T09:10:40.150Z Authors: Chaoyin She, Ruifang Lu, Danni He, Jiayi Lv, Yadan Lin, Meiqing Cheng, Hui Huang, Fengyu Ye, Lida Chen, Wei Wang, Qinghua Huang Published: 2025-02-06T04:17:02.000Z ## Abstract Hepatocellular carcinoma (HCC), ranking as the third leading cause of cancer-related mortality worldwide, demands urgent improvements in early detection to enhance patient survival. While ultrasound remains the preferred screening modality due to its cost-effectiveness and real-time capabilities, its sensitivity (59%-78%) heavily relies on radiologists' expertise, leading to inconsistent diagnostic outcomes and operational inefficiencies. Recent advancements in AI technology offer promising solutions to bridge this gap. This study introduces the Hierarchical Sparse Query Transformer (HSQformer), a novel hybrid architecture that synergizes CNNs' local feature extraction with Vision Transformers' global contextual awareness through latent space representation and sparse learning. By dynamically activating task-specific experts via a Mixture-of-Experts (MoE) framework, HSQformer achieves hierarchical feature integration without structural redundancy. Evaluated across three clinical scenarios: single-center, multi-center, and high-risk patient cohorts, HSQformer outperforms state-of-the-art models (e.g., 95.38% AUC in multi-center testing) and matches senior radiologists' diagnostic accuracy while significantly surpassing junior counterparts. These results highlight the potential of AI-assisted tools to standardize HCC screening, reduce dependency on human expertise, and improve early diagnosis rates. The full code is available at https://github.com/Asunatan/HSQformer. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Benign/Malignant Diagnosis on Single-Center (test)](): Benign/Malignant Diagnosis; Single-Center (test); Accuracy: 79.62. - [Benign/Malignant Diagnosis on Multi-Center (test)](): Benign/Malignant Diagnosis; Multi-Center (test); Accuracy: 88.51. - [Benign/Malignant Diagnosis on High-Risk Patient (test)](): Benign/Malignant Diagnosis; High-Risk Patient (test); Accuracy: 85.41. - [Diagnostic classification on High-risk patient (test)](): Diagnostic Classification; High-Risk Patient (test); Accuracy: 85.41. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()