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Volume: 28 | Article ID: art00007
Prostate cancer detection using photoacoustic imaging and deep learning
  DOI :  10.2352/ISSN.2470-1173.2016.15.IPAS-189  Published OnlineFebruary 2016

After skin cancer, prostate cancer is the most common cancer in American men. This paper introduces a new database which consists of a large sample size of patients gathered using multispectral photoacoustic imaging. As an alternate to the standard two class labeling (malignant, normal), our voxel based ground truth diagnosis consists of three classes (malignant, benign, normal). We explore deep neural nets, experiment with three popular activation functions, and perform different sub–feature group analysis. Our initial results serve as a benchmark on this database. Greedy based feature selection recognizes and eliminates noisy features. Ablation feature ranking at the feature and group level can simplify clinician effort and results are contrasted with medical literature. Our database is made freely available to the scientific community.

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Arjun Raj Rajanna, Raymond Ptucha, Saugata Sinha, Bhargava Chinni, Vikram Dogra, Navalgund A Rao, "Prostate cancer detection using photoacoustic imaging and deep learningin Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Processing: Algorithms and Systems XIV,  2016,

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