Oct 7, 2022 · We propose herewith a novel collaborative model to improve the trustworthiness of lung cancer predictions by self-regulation.
Sep 22, 2022 · We propose herewith a novel collaborative model to improve the trustworthiness of lung cancer predictions by self-regulation.
Objective The purpose of the present paper was to assess the effectiveness of integrating various machine-learning models with MRI biomarkers for lung cancer ...
Interpretable Lung Cancer Diagnosis with Nodule Attribute Guidance and Online Model Debugging ... model with margin ranking loss for lung nodule analysis.
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Interpretable Lung Cancer Diagnosis with Nodule Attribute Guidance and Online Model Debugging · Author Picture Hanxiao Zhang. Institute of Medical Robotics ...
Contents. Interpretable Lung Cancer Diagnosis with Nodule Attribute Guidance and Online Model Debugging. 1. Do Preprocessing and Augmentation Help ...
Interpretable Lung Cancer Diagnosis with Nodule Attribute Guidance and Online Model Debugging. H Zhang, L Chen, M Zhang, X Gu, Y Qin, W Yu, F Yao, Z Wang, Y ...
Accurate nodule labeling and interpretable machine learning are important for lung cancer diagnosis. To circumvent the label ambiguity issue of commonly-used ...
Sep 21, 2023 · An Interpretable Three-Dimensional Artificial Intelligence Model for Computer-Aided Diagnosis of Lung Nodules in Computed Tomography Images.
Missing: Attribute Guidance Online Debugging.
Interpretable Lung Cancer Diagnosis with Nodule Attribute Guidance and Online Model Debugging. H Zhang, L Chen, M Zhang, X Gu, Y Qin, W Yu, F Yao, Z Wang, Y ...