![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2640743%2Fc3043ea45f8d941f6e4ff618a6985e43%2FScreen%20Shot%202020-04-24%20at%2010.09.02%20AM.png?generation=1587744595444708&alt=media\")
\nSource: Automated Gleason Grading of Prostate Biopsies using Deep LearningHi Folks,
\n\nThese research articles seem to have great information about possibly setting up neural network architectures. For example:
\n\n\nSource: Automated Gleason Grading of Prostate Biopsies using Deep Learning
Authors: Wouter Bulten1,, Hans Pinckaers1, Hester van Boven2, Robert Vink3, Thomas de Bel1, Bram van Ginneken4, Jeroen van der Laak1, Christina Hulsbergen-van de Kaa3, and Geert Litjens1*
\n\nThis article was found by @dannellyz in his post here. Upvote it - great information!\nhttps://www.kaggle.com/c/prostate-cancer-grade-assessment/discussion/145253
\n\n--\nPapers with code also has a couple of other papers and repositories in addition to the above
\n\nLink: paperswithcode.com search for \"gleason\"
\n\n![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2640743%2Ffdd55c9b621aa91968756a5c704fa6f8%2FScreen%20Shot%202020-04-24%20at%2010.47.54%20AM.png?generation=1587746929752901&alt=media\")
--
\n\nSome other research articles that may be of interest.
\n\nProstate Cancer Detection using Deep Convolutional Neural Networks - https://www.nature.com/articles/s41598-019-55972-4
\n\nA new era: artificial intelligence and machine learning in prostate cancer - https://www.nature.com/articles/s41585-019-0193-3
\n\nMachine learning applications in prostate cancer magnetic resonance imaging - \nhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6686027/
\n\nProstate Cancer Diagnosis using Deep Learning with 3D Multiparametric MRI - \nhttps://arxiv.org/pdf/1703.04078.pdf
\n\nArtificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods - https://www.sciencedirect.com/science/article/pii/S0169260719310442
\n\nA classification model for the prostate cancer based on deep learning - https://ieeexplore.ieee.org/document/8302240
\n\nAlso see work from some previous competitions\nProstateX - 2019 with dataset\nhttps://wiki.cancerimagingarchive.net/display/Public/SPIE-AAPM-NCI+PROSTATEx+Challenges
\n\n--
\n\nThis is another fantastic post with more specific histopathological datasets and research found by @dannellyz . Upvote it - great information!\nhttps://www.kaggle.com/c/prostate-cancer-grade-assessment/discussion/145253
\n\n--\n*On the histopathology of prostate cancer: *
\n\nNormal prostate histology:\nhttps://webpath.med.utah.edu/TUTORIAL/PROSTATE/PROST001.html
\n\nHistopathology of Prostate Cancer - \nhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5629988/pdf/cshperspectmed-PCN-a030411.pdf
\n\nShorter article on histology of prostate cancer\nhttp://oncolex.org/Prostate-cancer/Background/Histology
\n\n--
\n\nThe Additional resources on the Kaggle page also recommend the following resources:
\n\n\"Computational Gleason grading.
\n\nFor recent reports on performing Gleason grading of biopsies automatically based on AI, see e.g. (Ström & Kartasalo, 2020) and (Bulten, 2020) or the corresponding freely available pre-print versions at https://arxiv.org/abs/1907.01368 and https://arxiv.org/abs/1907.07980.
\n\nReferences
\n\nBera et al., 2019. Artificial intelligence in digital pathology—new tools for diagnosis and precision oncology. Nature Reviews Clinical Oncology, 16(11), 703-715.
\n\nBulten et al., 2020. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. The Lancet Oncology.
\n\nEgevad et al., 2013. Standardization of Gleason grading among 337 European pathologists. Histopathology, 62(2), 247-256.
\n\nEpstein et al, 2016. The 2014 International Society of Urological Pathology (ISUP) consensus conference on gleason grading of prostatic carcinoma definition of grading patterns and proposal for a new grading system. Am J Surg Pathol; 40: 244–52.
\n\nNiazi et al., 2019. Digital pathology and artificial intelligence. The Lancet Oncology, 20(5), e253-e261.
\n\nStröm & Kartasalo et al., 2020. Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study. The Lancet Oncology.\"
\n\nGood luck everyone!
\n-Tom
Hi Folks,
\n\nThese research articles seem to have great information about possibly setting up neural network architectures. For example:
\n\n\nSource: Automated Gleason Grading of Prostate Biopsies using Deep Learning
Authors: Wouter Bulten1,, Hans Pinckaers1, Hester van Boven2, Robert Vink3, Thomas de Bel1, Bram van Ginneken4, Jeroen van der Laak1, Christina Hulsbergen-van de Kaa3, and Geert Litjens1*
\n\nThis article was found by @dannellyz in his post here. Upvote it - great information!\nhttps://www.kaggle.com/c/prostate-cancer-grade-assessment/discussion/145253
\n\n--\nPapers with code also has a couple of other papers and repositories in addition to the above
\n\nLink: paperswithcode.com search for \"gleason\"
\n\n
--
\n\nSome other research articles that may be of interest.
\n\nProstate Cancer Detection using Deep Convolutional Neural Networks - https://www.nature.com/articles/s41598-019-55972-4
\n\nA new era: artificial intelligence and machine learning in prostate cancer - https://www.nature.com/articles/s41585-019-0193-3
\n\nMachine learning applications in prostate cancer magnetic resonance imaging - \nhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6686027/
\n\nProstate Cancer Diagnosis using Deep Learning with 3D Multiparametric MRI - \nhttps://arxiv.org/pdf/1703.04078.pdf
\n\nArtificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods - https://www.sciencedirect.com/science/article/pii/S0169260719310442
\n\nA classification model for the prostate cancer based on deep learning - https://ieeexplore.ieee.org/document/8302240
\n\nAlso see work from some previous competitions\nProstateX - 2019 with dataset\nhttps://wiki.cancerimagingarchive.net/display/Public/SPIE-AAPM-NCI+PROSTATEx+Challenges
\n\n--
\n\nThis is another fantastic post with more specific histopathological datasets and research found by @dannellyz . Upvote it - great information!\nhttps://www.kaggle.com/c/prostate-cancer-grade-assessment/discussion/145253
\n\n--\n*On the histopathology of prostate cancer: *
\n\nNormal prostate histology:\nhttps://webpath.med.utah.edu/TUTORIAL/PROSTATE/PROST001.html
\n\nHistopathology of Prostate Cancer - \nhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5629988/pdf/cshperspectmed-PCN-a030411.pdf
\n\nShorter article on histology of prostate cancer\nhttp://oncolex.org/Prostate-cancer/Background/Histology
\n\n--
\n\nThe Additional resources on the Kaggle page also recommend the following resources:
\n\n\"Computational Gleason grading.
\n\nFor recent reports on performing Gleason grading of biopsies automatically based on AI, see e.g. (Ström & Kartasalo, 2020) and (Bulten, 2020) or the corresponding freely available pre-print versions at https://arxiv.org/abs/1907.01368 and https://arxiv.org/abs/1907.07980.
\n\nReferences
\n\nBera et al., 2019. Artificial intelligence in digital pathology—new tools for diagnosis and precision oncology. Nature Reviews Clinical Oncology, 16(11), 703-715.
\n\nBulten et al., 2020. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. The Lancet Oncology.
\n\nEgevad et al., 2013. Standardization of Gleason grading among 337 European pathologists. Histopathology, 62(2), 247-256.
\n\nEpstein et al, 2016. The 2014 International Society of Urological Pathology (ISUP) consensus conference on gleason grading of prostatic carcinoma definition of grading patterns and proposal for a new grading system. Am J Surg Pathol; 40: 244–52.
\n\nNiazi et al., 2019. Digital pathology and artificial intelligence. The Lancet Oncology, 20(5), e253-e261.
\n\nStröm & Kartasalo et al., 2020. Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study. The Lancet Oncology.\"
\n\nGood luck everyone!
\n-Tom
@tpmeli thanks!
", "rawMarkdown": "@tpmeli thanks!", "replies": [ { "id": 819504, "postDate": "2020-04-24T16:29:20.007Z", "content": "😄
", "rawMarkdown": "😄 " } ] }, { "id": 1022668, "postDate": "2020-09-22T17:03:04.867Z", "content": "thanks for the information
\nplease, I have been trying to get the actual dataset of the PANDA competition. It seems the one currently available is quite huge. Please, is there any way to get the dataset? I am a novice and I wan to try my hands on this dataset
@tpmeli thanks!
", "votes": 0, "replies": [ { "id": 819504, "author_name": "Tom M", "author_url": "", "post_date": "2020-04-24T16:29:20.007000", "content": "😄
", "votes": 0, "replies": [] } ] }, { "id": 1022668, "author_name": "OPOKU ERIC", "author_url": "", "post_date": "2020-09-22T17:03:04.867000", "content": "thanks for the information
\nplease, I have been trying to get the actual dataset of the PANDA competition. It seems the one currently available is quite huge. Please, is there any way to get the dataset? I am a novice and I wan to try my hands on this dataset