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Lumisys Inc 200 laser scanner
SUMMARY OF IMAGE PROPERTIES OF THE MAMMOGRAPHY AND DBT BREAST IMAGES USED IN THIS STUDY.
200 Laser Scanner, supplied by Lumisys Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/200+laser+scanner/200+laser+scanner/pmc06812655-18-9-8
Average 90 stars, based on 1 article reviews
200 laser scanner - by Bioz Stars, 2026-09
90/100 stars

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1) Product Images from "Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning using Deep Neural Nets"

Article Title: Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning using Deep Neural Nets

Journal: IEEE transactions on medical imaging

doi: 10.1109/TMI.2018.2870343

SUMMARY OF IMAGE PROPERTIES OF THE MAMMOGRAPHY AND DBT BREAST IMAGES USED IN THIS STUDY.
Figure Legend Snippet: SUMMARY OF IMAGE PROPERTIES OF THE MAMMOGRAPHY AND DBT BREAST IMAGES USED IN THIS STUDY.

Techniques Used:

Related Articles

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Article Title: Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning using Deep Neural Nets
Article Snippet: DDSM-SFM , Digital Database for Screening Mammography , Lumisys 200 laser scanner , 12 bits , 0.05 × 0.05.

Article Title: Multi-task transfer learning deep convolutional neural network: Application to computer-aided diagnosis of breast cancer on mammograms
Article Snippet: SFM-DDSM , Digital Database for Screening Mammography , Lumisys 200 laser scanner , 1988 – 1999 , 12 bits , 0 – 3.6 , 50 × 50.

Article Title: Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning using Deep Neural Nets
Article Snippet: FOR EACH LESION IN A DBT VOLUME, FIVE UNIQUE ROIS CENTERED AT THE CENTRAL SLICE WERE EXTRACTED table ft1 table-wrap mode="anchored" t5 TABLE II caption a7 Data set Source Device Gray level range Pixel size (mm) Mammography UM-SFM University of Michigan Lumiscan85 laser scanner 12 bits 0.05 × 0.05 UM-DM University of Michigan GE Senographe 200D FFDM 14 bits 0.1 × 0.1 DDSM-SFM Digital Database for Screening Mammography Lumisys 200 laser scanner 12 bits 0.05 × 0.05 DBT UM-DBT University of Michigan GE GEN2 prototype DBT system 12 bits 0.1 × 0.1 a MGH-DBT Massachusetts General Hospital GE prototype DBT system 12 bits 0.1 × 0.1 a Open in a separate window a The DBT volumes were reconstructed at 1 mm slice spacing.

Article Title: Multi-task transfer learning deep convolutional neural network: Application to computer-aided diagnosis of breast cancer on mammograms
Article Snippet: The SFM-DDSM set were acquired using a Lumisys 200 laser scanner with an optical density range of 0–3.6.

Article Title: Evolutionary Pruning of Transfer Learned Deep Convolutional Neural Network for Breast Cancer Diagnosis in Digital Breast Tomosynthesis
Article Snippet: The SFM-DDSM set was digitized using a Lumisys 200 laser scanner with an optical density of 0-3.6.

Article Title: Multi-task transfer learning deep convolutional neural network: Application to computer-aided diagnosis of breast cancer on mammograms
Article Snippet: Detailed information of the method can be found elsewhere ( Chan et al. , 1995a ; Sahiner et al. , 1996 ), and our previous work ( Samala et al. , 2016 ) includes additional information on the results for background correction on SFM, DDSM, DM and digital breast tomosynthesis. table ft1 table-wrap mode="anchored" t5 caption a7 Data type Source Device Acquisition years Gray level range Optical density range Pixel Size (μm) SFM-UM University of Michigan Lumiscan 85 laser scanner 1990 – 2005 12 bits 0 – 4.0 50 × 50 SFM-DDSM Digital Database for Screening Mammography Lumisys 200 laser scanner 1988 – 1999 12 bits 0 – 3.6 50 × 50 DM-UM University of Michigan GE Senographe 2000D FFDM 2001 – 2006 14 bits NA 100 × 100 Open in a separate window Mammography data sets used in this study table ft1 table-wrap mode="anchored" t5 Table 2 caption a7 Data type No. of views No. of lesions No. of malignant No. of benign No. of ROIs after data augmentation Training Set SFM-UM 748 886 317 569 7,088 SFM-DDSM 277 322 191 131 2,576 DM-UM 310 337 96 241 2,696 Test Set SFM-UM 907 909 453 456 7,272 Total 2,242 2,454 1,057 1,397 19,632 Open in a separate window Data sets used in this study.

Article Title: Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography
Article Snippet: 34 The mammograms in the SFM-UM and SFM-USF sets were digitized using a Lumisys 200 laser scanner with an optical density (OD) range of 0–3.6 and a LUMISCAN 85 laser scanner with an OD range of 0–4.0, respectively, with 4096 gray levels.



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SUMMARY OF IMAGE PROPERTIES OF THE MAMMOGRAPHY AND DBT BREAST IMAGES USED IN THIS STUDY.
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SUMMARY OF IMAGE PROPERTIES OF THE MAMMOGRAPHY AND DBT BREAST IMAGES USED IN THIS STUDY.

Journal: IEEE transactions on medical imaging

Article Title: Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning using Deep Neural Nets

doi: 10.1109/TMI.2018.2870343

Figure Lengend Snippet: SUMMARY OF IMAGE PROPERTIES OF THE MAMMOGRAPHY AND DBT BREAST IMAGES USED IN THIS STUDY.

Article Snippet: DDSM-SFM , Digital Database for Screening Mammography , Lumisys 200 laser scanner , 12 bits , 0.05 × 0.05.

Techniques: