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Automatic Classification of Prostate Cancer Gleason Scores from Digitized Whole Slide Tissue Biopsies

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.cell.2015.10.025openalex-snapshot

The Molecular Taxonomy of Primary Prostate Cancer

inferredcites →https://doi.org/10.1016/j.compmedimag.2018.01.008openalex-snapshot

Automated analysis and classification of melanocytic tumor on skin whole slide images

inferredcites →https://doi.org/10.1016/j.micron.2017.03.004openalex-snapshot

Automatic measurement of melanoma depth of invasion in skin histopathological images

inferredcites →https://doi.org/10.1016/j.patcog.2016.08.032openalex-snapshot

Local binary features for texture classification: Taxonomy and experimental study

inferredcites →https://doi.org/10.1038/srep27988openalex-snapshot

Multi-class texture analysis in colorectal cancer histology

inferredcites →https://doi.org/10.1073/pnas.1505935112openalex-snapshot

Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images

inferredcites →https://doi.org/10.1093/bioinformatics/btx723openalex-snapshot

Identification of topological features in renal tumor microenvironment associated with patient survival

inferredcites →https://doi.org/10.1109/isbi.2010.5490096openalex-snapshot

Computer-aided gleason grading of prostate cancer histopathological images using texton forests

inferredcites →https://doi.org/10.1109/jbhi.2016.2565515openalex-snapshot

Visually Meaningful Histopathological Features for Automatic Grading of Prostate Cancer

inferredcites →https://doi.org/10.1109/tip.2010.2044957openalex-snapshot

A Completed Modeling of Local Binary Pattern Operator for Texture Classification

inferredcites →https://doi.org/10.1109/tip.2016.2522378openalex-snapshot

Median Robust Extended Local Binary Pattern for Texture Classification

inferredcites →https://doi.org/10.1109/tmi.2009.2012704openalex-snapshot

Automatic Classification for Pathological Prostate Images Based on Fractal Analysis

inferredcites →https://doi.org/10.1109/tmi.2014.2336883openalex-snapshot

Prostate Cancer Grading: Use of Graph Cut and Spatial Arrangement of Nuclei

inferredcites →https://doi.org/10.1109/tpami.2002.1017623openalex-snapshot

Multiresolution gray-scale and rotation invariant texture classification with local binary patterns

inferredcites →https://doi.org/10.1109/tsmc.1979.4310076openalex-snapshot

A Threshold Selection Method from Gray-Level Histograms

inferredcites →https://doi.org/10.1186/s13000-016-0478-2openalex-snapshot

Grading of prostatic adenocarcinoma: current state and prognostic implications

inferredcites →https://doi.org/10.4103/2153-3539.119005openalex-snapshot

OpenSlide: A vendor-neutral software foundation for digital pathology

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1016/j.jpi.2022.100105openalex-snapshot

Spatial heterogeneity and organization of tumor mutation burden with immune infiltrates within tumors based on whole slide images correlated with patient survival in bladder cancer

inferred← citeshttps://doi.org/10.1101/554261openalex-snapshot

Gaussian Process Based Heteroscedastic Noise Modeling for Tumor Mutation Burden Prediction from Whole Slide Images

inferred← citeshttps://doi.org/10.3390/app10228285openalex-snapshot

Deep Learning-Based Pixel-Wise Lesion Segmentation on Oral Squamous Cell Carcinoma Images

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