@article{Wang2017,
   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.},
   author = {H. Wang and Satish E. Viswanath and A. Madabhushi},
   doi = {10.1038/s41598-017-12569-z},
   issn = {20452322},
   issue = {1},
   journal = {Scientific Reports},
   title = {Discriminative scale learning (DiScrn): Applications to prostate cancer detection from MRI and needle biopsies},
   volume = {7},
   year = {2017},
}

{"_id":"oWBKfs9kRWnhC3wNe","bibbaseid":"wang-viswanath-madabhushi-discriminativescalelearningdiscrnapplicationstoprostatecancerdetectionfrommriandneedlebiopsies-2017","authorIDs":["4Bygm8ju5EJL5ymrQ","6QhxQnZANogYLvQK3","95yz33vzsQF4xkftF","9XZAWfqroLnhv8Yvi","MFZedjHxD2knXtEDJ","QcPeEw2JciXtbZFYv","h2B3dsR3oRk42DaEg","ikoetnssERNBFB49Z","kEjtqmTXpJD7idBiM","mKXojsM3x8movswn3","mTxR5EmKaPZT4FPRw","wqGyw5WWC92w63FBr","zmtHQr2MyDkT4KaPS","zy5Yt2FBfMrjEQLRW"],"author_short":["Wang, H.","Viswanath, S. E.","Madabhushi, A."],"bibdata":{"bibtype":"article","type":"article","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.","author":[{"firstnames":["H."],"propositions":[],"lastnames":["Wang"],"suffixes":[]},{"firstnames":["Satish","E."],"propositions":[],"lastnames":["Viswanath"],"suffixes":[]},{"firstnames":["A."],"propositions":[],"lastnames":["Madabhushi"],"suffixes":[]}],"doi":"10.1038/s41598-017-12569-z","issn":"20452322","issue":"1","journal":"Scientific Reports","title":"Discriminative scale learning (DiScrn): Applications to prostate cancer detection from MRI and needle biopsies","volume":"7","year":"2017","bibtex":"@article{Wang2017,\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. 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.},\n author = {H. Wang and Satish E. Viswanath and A. Madabhushi},\n doi = {10.1038/s41598-017-12569-z},\n issn = {20452322},\n issue = {1},\n journal = {Scientific Reports},\n title = {Discriminative scale learning (DiScrn): Applications to prostate cancer detection from MRI and needle biopsies},\n volume = {7},\n year = {2017},\n}\n","author_short":["Wang, H.","Viswanath, S. E.","Madabhushi, A."],"key":"Wang2017","id":"Wang2017","bibbaseid":"wang-viswanath-madabhushi-discriminativescalelearningdiscrnapplicationstoprostatecancerdetectionfrommriandneedlebiopsies-2017","role":"author","urls":{},"metadata":{"authorlinks":{"viswanath, s":"https://bibbase.org/service/mendeley/eaba325f-653b-3ee2-b960-0abd5146933e"}},"downloads":0},"bibtype":"article","creationDate":"2020-04-20T04:32:18.520Z","downloads":0,"keywords":[],"search_terms":["discriminative","scale","learning","discrn","applications","prostate","cancer","detection","mri","needle","biopsies","wang","viswanath","madabhushi"],"title":"Discriminative scale learning (DiScrn): Applications to prostate cancer detection from MRI and needle biopsies","year":2017,"biburl":"https://bibbase.org/f/w8Gpb3sdkT5KfcMbC/export.bib","dataSources":["EqsrWP7MGiqJ6MAPy","ya2CyA73rpZseyrZ8","2252seNhipfTmjEBQ","xGcAuoNZfYSSjMypc"]}