most citedMethodological Explainability Evaluation of an Interpretable Deep Learning Model for Post-Hepatectomy Liver Failure Prediction Incorporating Counterfactual Explanations and Layerwise Relevance Propagation: A Prospective In Silico Trial

3 citations · 3 across the 2 of their papers we have counts for

collaborators

2 papers

cs.CL2026

IDRBench: Benchmarking the Interactive Capabilities of Deep Research Agents

Yingchaojie Feng, Qiang Huang, Xiaoya Xie +4

Large Language Model (LLM)-based deep research agents perform multi-step reasoning, web exploration, and long-form report generation. In these long-horizon workflows, early deviati…

cs.CV2024★ 3 cited

Methodological Explainability Evaluation of an Interpretable Deep Learning Model for Post-Hepatectomy Liver Failure Prediction Incorporating Counterfactual Explanations and Layerwise Relevance Propagation: A Prospective In Silico Trial

Xian Zhong, Zohaib Salahuddin, Yi Chen +15

Artificial intelligence (AI)-based decision support systems have demonstrated value in predicting post-hepatectomy liver failure (PHLF) in hepatocellular carcinoma (HCC). However,…