2019
DOI: 10.1007/978-3-030-32226-7_73
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Probabilistic Radiomics: Ambiguous Diagnosis with Controllable Shape Analysis

Abstract: Radiomics analysis has achieved great success in recent years. However, conventional Radiomics analysis suffers from insufficiently expressive hand-crafted features. Recently, emerging deep learning techniques, e.g., convolutional neural networks (CNNs), dominate recent research in Computer-Aided Diagnosis (CADx). Unfortunately, as blackbox predictors, we argue that CNNs are "diagnosing" voxels (or pixels), rather than lesions; in other words, visual saliency from a trained CNN is not necessarily concentrated … Show more

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Cited by 20 publications
(11 citation statements)
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“…Radiomic flow is a complex process, and every aspect of the image acquisition, such as defining and contouring the regions of interests, and choosing the best features to be extracted and the proper statistics to be applied, remains challenging. Lately, explainable AI (XAI), using DNNs (deep neural networks), may help radiomics in classification and prediction in the clinical setting [ 174 , 175 ], and a controllable and explainable probabilistic radiomics framework was proposed, through which a 3D CNN feature is extracted upon the lesion region only and is used to approximate the ambiguity distribution over human experts [ 176 ]. These new features will have to be further validated.…”
Section: Discussionmentioning
confidence: 99%
“…Radiomic flow is a complex process, and every aspect of the image acquisition, such as defining and contouring the regions of interests, and choosing the best features to be extracted and the proper statistics to be applied, remains challenging. Lately, explainable AI (XAI), using DNNs (deep neural networks), may help radiomics in classification and prediction in the clinical setting [ 174 , 175 ], and a controllable and explainable probabilistic radiomics framework was proposed, through which a 3D CNN feature is extracted upon the lesion region only and is used to approximate the ambiguity distribution over human experts [ 176 ]. These new features will have to be further validated.…”
Section: Discussionmentioning
confidence: 99%
“…Numerous computer-assisted tools have been introduced based on image processing, conventional machine learning, as well as advanced CNN models. CNN-based features are indeed abstract, and there is no guarantee that the black-box models with limited supervision derive the classification results by looking at the nodule lesions [38,52]. Moreover, previous studies have shown that separating image features extracted from inside the nodule and from the region immediately outside the nodule could lead to better classification accuracy.…”
Section: Discussionmentioning
confidence: 99%
“…Following studies introduces sophisticated feature aggregation based on spatial graphs [13,10] or attention [24]. However, only a few studies have applied deep shape analysis in medical imaging scenarios [25,20,22].…”
Section: Rib Segmentation From a Viewpoint Of Point Cloudsmentioning
confidence: 99%