IndraLab

Statements

CALM1 binds ITIH2. 5 / 5
| 5
sparser
"By incorporating genetic algorithms and particle swarm optimization, alongside temporal convolutional networks and domain-adversarial learning, the model achieved 94.2 percent classification accuracy and demonstrated strong generalization across diverse datasets, while SHAP-CAM enhanced interpretability for clinical decision-making."
41305166
sparser
"We propose a new approach, SCIM (SHAP-CAM Interpretable Mapping), which has shown promising results."
39700858
sparser
"In the SHAP-CAM heatmap, the predictive referable lesion visualisation not only showed their located domain, but also the shape of the lesions, which were more fined-discriminative than the CAM heatmaps and with less noise than the DeepSHAP ( xref )."
35902186
sparser
"Our results demonstrated that (1) the five-dimensional classifiers (image quality, retinopathy, maculopathy gradability, maculopathy and photocoagulation) achieved high accuracy in each classification; (2) a three-level referable DR decision (image, eye and patient level) could be automatically generated by the DL platform; and (3) visualisation by the SHAP-CAM heatmaps provided the explainability for the referable lesion prediction from the platform."
35902186
sparser
"The SHAP-CAM heatmap highlights the predictive referable DR lesions on retinal fundus pictures."
35902186