We develop a soft activation mapping (SAM) method to enable fine-grained lung nodule shape and margin (LNSM) features analysis with a CNN.

In this paper, we first develop a soft activation mapping (SAM) to enable fine-grained lung nodule shape & margin (LNSM) feature analysis with a CNN so that it ...

Sep 8, 2024 · In this paper, we first develop a soft activation mapping (SAM) to enable fine-grained lung nodule shape & margin (LNSM) feature analysis with a ...

A soft activation mapping (SAM) to enable fine-grained lesion analysis with a CNN so that it can access rich radiomics features and a high-level feature ...

We develop a soft activation mapping (SAM) method to enable fine-grained lung nodule shape and margin (LNSM) features analysis with a CNN.

Shape and margin-aware lung nodule classification in low-dose CT images via soft activation mapping · 72 Citations · 56 References.

Nov 28, 2023 · In this paper, we propose a novel fusion algorithm, RGD, for benign-malignant lung nodule classification by incorporating Radiomics study and Graph learning ...

Mar 25, 2021 · The aim of this study is to propose a method for predicting nodule malignancy based on deep abstract features.

To make the learned deep features interpretable, Lei et al. (15) developed a Soft Activation Mapping (SAM) to enable the analysis of fine-grained lung nodule ...