@article{
 title = {Discriminative scale learning (DiScrn): Applications to prostate cancer detection from MRI and needle biopsies},
 type = {article},
 year = {2017},
 volume = {7},
 id = {f523a33c-fe34-30ad-a22f-9c052c08cafb},
 created = {2023-10-25T08:56:39.966Z},
 file_attached = {false},
 profile_id = {eaba325f-653b-3ee2-b960-0abd5146933e},
 last_modified = {2023-10-25T08:56:39.966Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {true},
 abstract = {There has been recent substantial interest in extracting sub-visual features from medical images for improved disease characterization compared to what might be achievable via visual inspection alone. Features such as Haralick and Gabor can provide a multi-scale representation of the original image by extracting measurements across differently sized neighborhoods. While these multi-scale features are effective, on large-scale digital pathological images, the process of extracting these features is computationally expensive. Moreover for different problems, different scales and neighborhood sizes may be more or less important and thus a large number of features extracted might end up being redundant. In this paper, we present a Discriminative Scale learning (DiScrn) approach that attempts to automatically identify the distinctive scales at which features are able to best separate cancerous from non-cancerous regions on both radiologic and digital pathology tissue images. To evaluate the efficacy of our approach, our approach was employed to detect presence and extent of prostate cancer on a total of 60 MRI and digitized histopathology images. Compared to a multi-scale feature analysis approach invoking features across all scales, DiScrn achieved 66% computational efficiency while also achieving comparable or even better classifier performance.},
 bibtype = {article},
 author = {Wang, H. and Viswanath, S. and Madabhushi, A.},
 doi = {10.1038/s41598-017-12569-z},
 journal = {Scientific Reports},
 number = {1}
}

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Compared to a multi-scale feature analysis approach invoking features across all scales, DiScrn achieved 66% computational efficiency while also achieving comparable or even better classifier performance.","bibtype":"article","author":"Wang, H. and Viswanath, S. and Madabhushi, A.","doi":"10.1038/s41598-017-12569-z","journal":"Scientific Reports","number":"1","bibtex":"@article{\n title = {Discriminative scale learning (DiScrn): Applications to prostate cancer detection from MRI and needle biopsies},\n type = {article},\n year = {2017},\n volume = {7},\n id = {f523a33c-fe34-30ad-a22f-9c052c08cafb},\n created = {2023-10-25T08:56:39.966Z},\n file_attached = {false},\n profile_id = {eaba325f-653b-3ee2-b960-0abd5146933e},\n last_modified = {2023-10-25T08:56:39.966Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n private_publication = {true},\n abstract = {There has been recent substantial interest in extracting sub-visual features from medical images for improved disease characterization compared to what might be achievable via visual inspection alone. 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