license: gpl-3.0
Dataset Card for v0_DUV-FSM_Breast-Mixed
Dataset containing breast tissue imaging data, captured using a DUV-FSM (Deep-Ultraviolet Scanning Fluorescence Microscope).
Includes annotated 400x400 lossless/.tif patches extracted from compressed/.jpg WSI from the same samples (Referenced as S2-S70).
Dataset Details
Dataset Description
- Funded by: NIH Grant 5R01EB033806
- License: GPL-3.0
- Data Details: https://pmc.ncbi.nlm.nih.gov/articles/PMC9484420/pdf/boe-13-9-5015.pdf
Archived in August 2024, this is intended to be a historical/archived/referential copy and not for future/active research.
Code Repositories
- Original: github.com/tyrellto/breast-cancer-research/tree/main
- Updated: github.com/Yatagarasu50469/RANDS
Dataset Structure
v0_DUV-FSM_Breast-Mixed/ |----->Patches/ | |----->S*/ | | |----->PS*_*.tif |----->WSI/ | |----->*.jpg
Citations
Prior/Original Classification Network
Subject: Breast Cancer Classification for DUV-FSM
Available: (https://github.com/tyrellto/breast-cancer-research/tree/main)
Note: Original classification network code for RANDS (Updated code repository) was derived from this existing work (published under GNU GPLv3), but entirely rewritten.
Prior/Original and Diffusion Network
Subject: Breast Cancer Classification for DUV-FSM
Citation(s): G. S. Salem, T. To, J. Jorns, T. Yen, B. Yu, and D. H. Ye, “Deep learning for automated detection of breast cancer in deep ultraviolet fluorescence images with diffusion probabilistic model,” arXiv (Cornell University), Jul. 2024, doi: https://doi.org/10.1109/isbi56570.2024.10635349.
Available: (https://pubmed.ncbi.nlm.nih.gov/40313564/)
Prior/Original Classification Network
Subject: Breast Cancer Classification for DUV-FSM
Citation(s): T. To et al., “Deep learning classification of deep ultraviolet fluorescence images toward intra-operative margin assessment in breast cancer,” Frontiers in Oncology, vol. 13, Jun. 2023, doi: 10.3389/fonc.2023.1179025
Available: (https://pmc.ncbi.nlm.nih.gov/articles/PMC10313133/)
Prior/Original Classification Network
Subject: Breast Cancer Classification for DUV-FSM
Citation(s): Lu T, Jorns JM, Ye DH, Patton M, Gilat-Schmidt T, Yen T, Yu B. Analysis of Deep Ultraviolet Fluorescence Images for Intraoperative Breast Tumor Margin Assessment. Proc SPIE Int Soc Opt Eng. 2023 Jan-Feb;12368:1236806. doi: 10.1117/12.2649552. Epub 2023 Mar 6. PMID: 37292087; PMCID: PMC10249647.
Available: (https://pmc.ncbi.nlm.nih.gov/articles/PMC10249647/)
Prior/Original Classification Network
Subject: Breast Cancer Classification for DUV-FSM
Citation(s): T. To, “Deep Learning Classification of Deep Ultraviolet Fluorescence Images for Margin Assessment During Breast Cancer Surgery,” Master’s Thesis, Marquette University, 2023.
Available: (https://epublications.marquette.edu/theses_open/768)
Texture Analysis Classification
Subject: Breast Cancer Classification for DUV-FSM
Citation(s): Lu T, Jorns JM, Ye DH, Patton M, Fisher R, Emmrich A, Schmidt TG, Yen T, Yu B. Automated assessment of breast margins in deep ultraviolet fluorescence images using texture analysis. Biomed Opt Express. 2022 Aug 30;13(9):5015-5034. doi: 10.1364/BOE.464547. PMID: 36187258; PMCID: PMC9484420.
Available: (https://pmc.ncbi.nlm.nih.gov/articles/PMC9484420/)
Prior/Original Classification Network
Subject: Breast Cancer Classification for DUV-FSM
Citation(s): T. To, S. H. Gheshlaghi and D. H. Ye, "Deep Learning for Breast Cancer Classification of Deep Ultraviolet Fluorescence Images toward Intra-Operative Margin Assessment," 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, Scotland, United Kingdom, 2022, pp. 1891-1894, doi: 10.1109/EMBC48229.2022.9871819
Available: (https://doi.org/10.1109/EMBC48229.2022.9871819)
Experimental DUV-FSM Platform
Subject: First Generation Experimental Platform for DUV-FSM
Citation(s): Lu T, Jorns JM, Patton M, Fisher R, Emmrich A, Doehring T, Schmidt TG, Ye DH, Yen T, Yu B. Rapid assessment of breast tumor margins using deep ultraviolet fluorescence scanning microscopy. J Biomed Opt. 2020 Nov;25(12):126501. doi: 10.1117/1.JBO.25.12.126501. PMID: 33241673; PMCID: PMC7688317.
Available: (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7688317/)