Big Data Resources for Digital Pathology

S. S. Shalamzari; M. Bagritsevich; A. Melles; I. Obeid; J. Picone; D. Connolly; C. Wu; B. Schultz; B. Brown; J. James; Y. Gong; H. Wu

doi:10.1109/SPMB59478.2023.10372721

Big Data Resources for Digital Pathology

S. S. Shalamzari, M. Bagritsevich, A. Melles, I. Obeid, J. Picone, D. Connolly, C. Wu, B. Schultz, B. Brown, J. James, Y. Gong, H. Wu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The Neural Engineering Data Consortium (NEDC), known for its open source data resources [1], has previously released the Breast Tissue subset of the Temple University Digital Pathology Corpus (TUDP), which consists of 3,505 partially annotated images [2]. This corpus is part of a much larger repository of over 100,000 images that will be released as part of NEDC's digital pathology resources in 2023. In this abstract, we introduce our recently released corpus of 14,288 digital pathology images that were collected from Fox Chase Cancer Center's (FCCC) Biosample Repository [3], and describe some changes being made to TUDP to organize these corpora in a unified framework.

Original language	English
Title of host publication	2023 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2023 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350341256
DOIs	https://doi.org/10.1109/SPMB59478.2023.10372721
State	Published - 2023
Event	2023 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2023 - Philadelphia, United States Duration: Dec 2 2023 → …

Publication series

Name	2023 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2023 - Proceedings

Conference

Conference	2023 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2023
Country/Territory	United States
City	Philadelphia
Period	12/2/23 → …

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/SPMB59478.2023.10372721

Cite this

Shalamzari, S. S., Bagritsevich, M., Melles, A., Obeid, I., Picone, J., Connolly, D., Wu, C., Schultz, B., Brown, B., James, J., Gong, Y., & Wu, H. (2023). Big Data Resources for Digital Pathology. In 2023 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2023 - Proceedings (2023 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2023 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SPMB59478.2023.10372721

@inproceedings{01e9aadc1adf42bea5d5eb60ff4d099b,

title = "Big Data Resources for Digital Pathology",

abstract = "The Neural Engineering Data Consortium (NEDC), known for its open source data resources [1], has previously released the Breast Tissue subset of the Temple University Digital Pathology Corpus (TUDP), which consists of 3,505 partially annotated images [2]. This corpus is part of a much larger repository of over 100,000 images that will be released as part of NEDC's digital pathology resources in 2023. In this abstract, we introduce our recently released corpus of 14,288 digital pathology images that were collected from Fox Chase Cancer Center's (FCCC) Biosample Repository [3], and describe some changes being made to TUDP to organize these corpora in a unified framework.",

author = "Shalamzari, {S. S.} and M. Bagritsevich and A. Melles and I. Obeid and J. Picone and D. Connolly and C. Wu and B. Schultz and B. Brown and J. James and Y. Gong and H. Wu",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2023 ; Conference date: 02-12-2023",

year = "2023",

doi = "10.1109/SPMB59478.2023.10372721",

language = "English",

series = "2023 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2023 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2023 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2023 - Proceedings",

address = "United States",

}

Shalamzari, SS, Bagritsevich, M, Melles, A, Obeid, I, Picone, J, Connolly, D, Wu, C, Schultz, B, Brown, B, James, J, Gong, Y & Wu, H 2023, Big Data Resources for Digital Pathology. in 2023 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2023 - Proceedings. 2023 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2023 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2023 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2023, Philadelphia, United States, 12/2/23. https://doi.org/10.1109/SPMB59478.2023.10372721

Big Data Resources for Digital Pathology. / Shalamzari, S. S.; Bagritsevich, M.; Melles, A. et al.
2023 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2023 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Big Data Resources for Digital Pathology

AU - Shalamzari, S. S.

AU - Bagritsevich, M.

AU - Melles, A.

AU - Obeid, I.

AU - Picone, J.

AU - Connolly, D.

AU - Wu, C.

AU - Schultz, B.

AU - Brown, B.

AU - James, J.

AU - Gong, Y.

AU - Wu, H.

PY - 2023

Y1 - 2023

N2 - The Neural Engineering Data Consortium (NEDC), known for its open source data resources [1], has previously released the Breast Tissue subset of the Temple University Digital Pathology Corpus (TUDP), which consists of 3,505 partially annotated images [2]. This corpus is part of a much larger repository of over 100,000 images that will be released as part of NEDC's digital pathology resources in 2023. In this abstract, we introduce our recently released corpus of 14,288 digital pathology images that were collected from Fox Chase Cancer Center's (FCCC) Biosample Repository [3], and describe some changes being made to TUDP to organize these corpora in a unified framework.

AB - The Neural Engineering Data Consortium (NEDC), known for its open source data resources [1], has previously released the Breast Tissue subset of the Temple University Digital Pathology Corpus (TUDP), which consists of 3,505 partially annotated images [2]. This corpus is part of a much larger repository of over 100,000 images that will be released as part of NEDC's digital pathology resources in 2023. In this abstract, we introduce our recently released corpus of 14,288 digital pathology images that were collected from Fox Chase Cancer Center's (FCCC) Biosample Repository [3], and describe some changes being made to TUDP to organize these corpora in a unified framework.

UR - http://www.scopus.com/inward/record.url?scp=85183459118&partnerID=8YFLogxK

U2 - 10.1109/SPMB59478.2023.10372721

DO - 10.1109/SPMB59478.2023.10372721

M3 - Conference contribution

AN - SCOPUS:85183459118

T3 - 2023 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2023 - Proceedings

BT - 2023 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2023 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2023

Y2 - 2 December 2023

ER -

Shalamzari SS, Bagritsevich M, Melles A, Obeid I, Picone J, Connolly D et al. Big Data Resources for Digital Pathology. In 2023 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2023 - Proceedings). doi: 10.1109/SPMB59478.2023.10372721

Big Data Resources for Digital Pathology

Abstract

Publication series

Conference

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this