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The revolving door for AI and pathologists-docendo discimus?.

Journal of medical artificial intelligence

Van Es SL, Madabhushi A.
PMID: 31372599
J Med Artif Intell. 2019 Jun;2. doi: 10.21037/jmai.2019.05.02. Epub 2019 Jun 11.

No abstract available.

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Van Es SL, Madabhushi A. The revolving door for AI and pathologists-docendo discimus?. J Med Artif Intell. 2019;2doi: 10.21037/jmai.2019.05.02.
Van Es, S. L., & Madabhushi, A. (2019). The revolving door for AI and pathologists-docendo discimus?. Journal of medical artificial intelligence, 2. https://doi.org/10.21037/jmai.2019.05.02
Van Es, Simone L, and Madabhushi, Anant. "The revolving door for AI and pathologists-docendo discimus?." Journal of medical artificial intelligence vol. 2 (2019). doi: https://doi.org/10.21037/jmai.2019.05.02
Van Es SL, Madabhushi A. The revolving door for AI and pathologists-docendo discimus?. J Med Artif Intell. 2019 Jun;2. doi: 10.21037/jmai.2019.05.02. Epub 2019 Jun 11. PMID: 31372599; PMCID: PMC6675021.
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Collagen fiber orientation disorder from H&E images is prognostic for early stage breast cancer: clinical trial validation.

NPJ breast cancer

Li H, Bera K, Toro P, Fu P, Zhang Z, Lu C, Feldman M, Ganesan S, Goldstein LJ, Davidson NE, Glasgow A, Harbhajanka A, Gilmore H, Madabhushi A.
PMID: 34362928
NPJ Breast Cancer. 2021 Aug 06;7(1):104. doi: 10.1038/s41523-021-00310-z.

Collagen fiber organization has been found to be implicated in breast cancer prognosis. In this study, we evaluated whether computerized features of Collagen Fiber Orientation Disorder in Tumor-associated Stroma (CFOD-TS) on Hematoxylin & Eosin (H&E) slide images were prognostic...

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Li H, Bera K, Toro P, et al. Collagen fiber orientation disorder from H&E images is prognostic for early stage breast cancer: clinical trial validation. NPJ Breast Cancer. 2021;7(1):104doi: 10.1038/s41523-021-00310-z.
Li, H., Bera, K., Toro, P., Fu, P., Zhang, Z., Lu, C., Feldman, M., Ganesan, S., Goldstein, L. J., Davidson, N. E., Glasgow, A., Harbhajanka, A., Gilmore, H., & Madabhushi, A. (2021). Collagen fiber orientation disorder from H&E images is prognostic for early stage breast cancer: clinical trial validation. NPJ breast cancer, 7(1), 104. https://doi.org/10.1038/s41523-021-00310-z
Li, Haojia, et al. "Collagen fiber orientation disorder from H&E images is prognostic for early stage breast cancer: clinical trial validation." NPJ breast cancer vol. 7,1 (2021): 104. doi: https://doi.org/10.1038/s41523-021-00310-z
Li H, Bera K, Toro P, Fu P, Zhang Z, Lu C, Feldman M, Ganesan S, Goldstein LJ, Davidson NE, Glasgow A, Harbhajanka A, Gilmore H, Madabhushi A. Collagen fiber orientation disorder from H&E images is prognostic for early stage breast cancer: clinical trial validation. NPJ Breast Cancer. 2021 Aug 06;7(1):104. doi: 10.1038/s41523-021-00310-z. PMID: 34362928; PMCID: PMC8346522.
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Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study.

Cancers

Algohary A, Shiradkar R, Pahwa S, Purysko A, Verma S, Moses D, Shnier R, Haynes AM, Delprado W, Thompson J, Tirumani S, Mahran A, Rastinehad AR, Ponsky L, Stricker PD, Madabhushi A.
PMID: 32781640
Cancers (Basel). 2020 Aug 06;12(8). doi: 10.3390/cancers12082200.

No abstract available.

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Algohary A, Shiradkar R, Pahwa S, et al. Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study. Cancers (Basel). 2020;12(8)doi: 10.3390/cancers12082200.
Algohary, A., Shiradkar, R., Pahwa, S., Purysko, A., Verma, S., Moses, D., Shnier, R., Haynes, A. M., Delprado, W., Thompson, J., Tirumani, S., Mahran, A., Rastinehad, A. R., Ponsky, L., Stricker, P. D., & Madabhushi, A. (2020). Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study. Cancers, 12(8), . https://doi.org/10.3390/cancers12082200
Algohary, Ahmad, et al. "Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study." Cancers vol. 12,8 (2020). doi: https://doi.org/10.3390/cancers12082200
Algohary A, Shiradkar R, Pahwa S, Purysko A, Verma S, Moses D, Shnier R, Haynes AM, Delprado W, Thompson J, Tirumani S, Mahran A, Rastinehad AR, Ponsky L, Stricker PD, Madabhushi A. Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study. Cancers (Basel). 2020 Aug 06;12(8). doi: 10.3390/cancers12082200. PMID: 32781640; PMCID: PMC7465024.
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Quick Annotator: an open-source digital pathology based rapid image annotation tool.

The journal of pathology. Clinical research

Miao R, Toth R, Zhou Y, Madabhushi A, Janowczyk A.
PMID: 34288586
J Pathol Clin Res. 2021 Nov;7(6):542-547. doi: 10.1002/cjp2.229. Epub 2021 Jul 19.

Image-based biomarker discovery typically requires accurate segmentation of histologic structures (e.g. cell nuclei, tubules, and epithelial regions) in digital pathology whole slide images (WSIs). Unfortunately, annotating each structure of interest is laborious and often intractable even in moderately sized...

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Miao R, Toth R, Zhou Y, et al. Quick Annotator: an open-source digital pathology based rapid image annotation tool. J Pathol Clin Res. 2021;7(6):542-547doi: 10.1002/cjp2.229.
Miao, R., Toth, R., Zhou, Y., Madabhushi, A., & Janowczyk, A. (2021). Quick Annotator: an open-source digital pathology based rapid image annotation tool. The journal of pathology. Clinical research, 7(6), 542-547. https://doi.org/10.1002/cjp2.229
Miao, Runtian, et al. "Quick Annotator: an open-source digital pathology based rapid image annotation tool." The journal of pathology. Clinical research vol. 7,6 (2021): 542-547. doi: https://doi.org/10.1002/cjp2.229
Miao R, Toth R, Zhou Y, Madabhushi A, Janowczyk A. Quick Annotator: an open-source digital pathology based rapid image annotation tool. J Pathol Clin Res. 2021 Nov;7(6):542-547. doi: 10.1002/cjp2.229. Epub 2021 Jul 19. PMID: 34288586; PMCID: PMC8503896.
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Computationally Derived Cribriform Area Index from Prostate Cancer Hematoxylin and Eosin Images Is Associated with Biochemical Recurrence Following Radical Prostatectomy and Is Most Prognostic in Gleason Grade Group 2.

European urology focus

Leo P, Chandramouli S, Farré X, Elliott R, Janowczyk A, Bera K, Fu P, Janaki N, El-Fahmawi A, Shahait M, Kim J, Lee D, Yamoah K, Rebbeck TR, Khani F, Robinson BD, Shih NNC, Feldman M, Gupta S, McKenney J, Lal P, Madabhushi A.
PMID: 33941504
Eur Urol Focus. 2021 Jul;7(4):722-732. doi: 10.1016/j.euf.2021.04.016. Epub 2021 Apr 30.

BACKGROUND: The presence of invasive cribriform adenocarcinoma (ICC), an expanse of cells containing punched-out lumina uninterrupted by stroma, in radical prostatectomy (RP) specimens has been associated with biochemical recurrence (BCR). However, ICC identification has only moderate inter-reviewer agreement.OBJECTIVE: To...

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Leo P, Chandramouli S, Farré X, et al. Computationally Derived Cribriform Area Index from Prostate Cancer Hematoxylin and Eosin Images Is Associated with Biochemical Recurrence Following Radical Prostatectomy and Is Most Prognostic in Gleason Grade Group 2. Eur Urol Focus. 2021;7(4):722-732doi: 10.1016/j.euf.2021.04.016.
Leo, P., Chandramouli, S., Farré, X., Elliott, R., Janowczyk, A., Bera, K., Fu, P., Janaki, N., El-Fahmawi, A., Shahait, M., Kim, J., Lee, D., Yamoah, K., Rebbeck, T. R., Khani, F., Robinson, B. D., Shih, N. N. C., Feldman, M., Gupta, S., McKenney, J., Lal, P., & Madabhushi, A. (2021). Computationally Derived Cribriform Area Index from Prostate Cancer Hematoxylin and Eosin Images Is Associated with Biochemical Recurrence Following Radical Prostatectomy and Is Most Prognostic in Gleason Grade Group 2. European urology focus, 7(4), 722-732. https://doi.org/10.1016/j.euf.2021.04.016
Leo, Patrick, et al. "Computationally Derived Cribriform Area Index from Prostate Cancer Hematoxylin and Eosin Images Is Associated with Biochemical Recurrence Following Radical Prostatectomy and Is Most Prognostic in Gleason Grade Group 2." European urology focus vol. 7,4 (2021): 722-732. doi: https://doi.org/10.1016/j.euf.2021.04.016
Leo P, Chandramouli S, Farré X, Elliott R, Janowczyk A, Bera K, Fu P, Janaki N, El-Fahmawi A, Shahait M, Kim J, Lee D, Yamoah K, Rebbeck TR, Khani F, Robinson BD, Shih NNC, Feldman M, Gupta S, McKenney J, Lal P, Madabhushi A. Computationally Derived Cribriform Area Index from Prostate Cancer Hematoxylin and Eosin Images Is Associated with Biochemical Recurrence Following Radical Prostatectomy and Is Most Prognostic in Gleason Grade Group 2. Eur Urol Focus. 2021 Jul;7(4):722-732. doi: 10.1016/j.euf.2021.04.016. Epub 2021 Apr 30. PMID: 33941504; PMCID: PMC8419103.
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Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography.

Journal of medical imaging (Bellingham, Wash.)

Orooji M, Alilou M, Rakshit S, Beig N, Khorrami MH, Rajiah P, Thawani R, Ginsberg J, Donatelli C, Yang M, Jacono F, Gilkeson R, Velcheti V, Linden P, Madabhushi A.
PMID: 29721515
J Med Imaging (Bellingham). 2018 Apr;5(2):024501. doi: 10.1117/1.JMI.5.2.024501. Epub 2018 Apr 18.

Differentiation between benign and malignant nodules is a problem encountered by radiologists when visualizing computed tomography (CT) scans. Adenocarcinomas and granulomas have a characteristic spiculated appearance and may be fluorodeoxyglucose avid, making them difficult to distinguish for human readers....

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Orooji M, Alilou M, Rakshit S, et al. Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography. J Med Imaging (Bellingham). 2018;5(2):024501doi: 10.1117/1.JMI.5.2.024501.
Orooji, M., Alilou, M., Rakshit, S., Beig, N., Khorrami, M. H., Rajiah, P., Thawani, R., Ginsberg, J., Donatelli, C., Yang, M., Jacono, F., Gilkeson, R., Velcheti, V., Linden, P., & Madabhushi, A. (2018). Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography. Journal of medical imaging (Bellingham, Wash.), 5(2), 024501. https://doi.org/10.1117/1.JMI.5.2.024501
Orooji, Mahdi, et al. "Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography." Journal of medical imaging (Bellingham, Wash.) vol. 5,2 (2018): 024501. doi: https://doi.org/10.1117/1.JMI.5.2.024501
Orooji M, Alilou M, Rakshit S, Beig N, Khorrami MH, Rajiah P, Thawani R, Ginsberg J, Donatelli C, Yang M, Jacono F, Gilkeson R, Velcheti V, Linden P, Madabhushi A. Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography. J Med Imaging (Bellingham). 2018 Apr;5(2):024501. doi: 10.1117/1.JMI.5.2.024501. Epub 2018 Apr 18. PMID: 29721515; PMCID: PMC5904542.
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Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings.

Journal of magnetic resonance imaging : JMRI

Algohary A, Viswanath S, Shiradkar R, Ghose S, Pahwa S, Moses D, Jambor I, Shnier R, Böhm M, Haynes AM, Brenner P, Delprado W, Thompson J, Pulbrock M, Purysko AS, Verma S, Ponsky L, Stricker P, Madabhushi A.
PMID: 29469937
J Magn Reson Imaging. 2018 Feb 22; doi: 10.1002/jmri.25983. Epub 2018 Feb 22.

BACKGROUND: Radiomic analysis is defined as computationally extracting features from radiographic images for quantitatively characterizing disease patterns. There has been recent interest in examining the use of MRI for identifying prostate cancer (PCa) aggressiveness in patients on active surveillance...

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Algohary A, Viswanath S, Shiradkar R, et al. Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings. J Magn Reson Imaging. 2018;doi: 10.1002/jmri.25983.
Algohary, A., Viswanath, S., Shiradkar, R., Ghose, S., Pahwa, S., Moses, D., Jambor, I., Shnier, R., Böhm, M., Haynes, A. M., Brenner, P., Delprado, W., Thompson, J., Pulbrock, M., Purysko, A. S., Verma, S., Ponsky, L., Stricker, P., & Madabhushi, A. (2018). Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings. Journal of magnetic resonance imaging : JMRI, . https://doi.org/10.1002/jmri.25983
Algohary, Ahmad, et al. "Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings." Journal of magnetic resonance imaging : JMRI vol. (2018). doi: https://doi.org/10.1002/jmri.25983
Algohary A, Viswanath S, Shiradkar R, Ghose S, Pahwa S, Moses D, Jambor I, Shnier R, Böhm M, Haynes AM, Brenner P, Delprado W, Thompson J, Pulbrock M, Purysko AS, Verma S, Ponsky L, Stricker P, Madabhushi A. Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings. J Magn Reson Imaging. 2018 Feb 22; doi: 10.1002/jmri.25983. Epub 2018 Feb 22. PMID: 29469937; PMCID: PMC6105554.
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Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI.

Journal of medical imaging (Bellingham, Wash.)

Chirra P, Leo P, Yim M, Bloch BN, Rastinehad AR, Purysko A, Rosen M, Madabhushi A, Viswanath SE.
PMID: 31259199
J Med Imaging (Bellingham). 2019 Apr;6(2):024502. doi: 10.1117/1.JMI.6.2.024502. Epub 2019 Jun 14.

Recent advances in the field of radiomics have enabled the development of a number of prognostic and predictive imaging-based tools for a variety of diseases. However, wider clinical adoption of these tools is contingent on their generalizability across multiple...

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Chirra P, Leo P, Yim M, et al. Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI. J Med Imaging (Bellingham). 2019;6(2):024502doi: 10.1117/1.JMI.6.2.024502.
Chirra, P., Leo, P., Yim, M., Bloch, B. N., Rastinehad, A. R., Purysko, A., Rosen, M., Madabhushi, A., & Viswanath, S. E. (2019). Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI. Journal of medical imaging (Bellingham, Wash.), 6(2), 024502. https://doi.org/10.1117/1.JMI.6.2.024502
Chirra, Prathyush, et al. "Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI." Journal of medical imaging (Bellingham, Wash.) vol. 6,2 (2019): 024502. doi: https://doi.org/10.1117/1.JMI.6.2.024502
Chirra P, Leo P, Yim M, Bloch BN, Rastinehad AR, Purysko A, Rosen M, Madabhushi A, Viswanath SE. Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI. J Med Imaging (Bellingham). 2019 Apr;6(2):024502. doi: 10.1117/1.JMI.6.2.024502. Epub 2019 Jun 14. PMID: 31259199; PMCID: PMC6566001.
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Quality control stress test for deep learning-based diagnostic model in digital pathology.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc

Schömig-Markiefka B, Pryalukhin A, Hulla W, Bychkov A, Fukuoka J, Madabhushi A, Achter V, Nieroda L, Büttner R, Quaas A, Tolkach Y.
PMID: 34168282
Mod Pathol. 2021 Dec;34(12):2098-2108. doi: 10.1038/s41379-021-00859-x. Epub 2021 Jun 24.

Digital pathology provides a possibility for computational analysis of histological slides and automatization of routine pathological tasks. Histological slides are very heterogeneous concerning staining, sections' thickness, and artifacts arising during tissue processing, cutting, staining, and digitization. In this study,...

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Schömig-Markiefka B, Pryalukhin A, Hulla W, et al. Quality control stress test for deep learning-based diagnostic model in digital pathology. Mod Pathol. 2021;34(12):2098-2108doi: 10.1038/s41379-021-00859-x.
Schömig-Markiefka, B., Pryalukhin, A., Hulla, W., Bychkov, A., Fukuoka, J., Madabhushi, A., Achter, V., Nieroda, L., Büttner, R., Quaas, A., & Tolkach, Y. (2021). Quality control stress test for deep learning-based diagnostic model in digital pathology. Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc, 34(12), 2098-2108. https://doi.org/10.1038/s41379-021-00859-x
Schömig-Markiefka, Birgid, et al. "Quality control stress test for deep learning-based diagnostic model in digital pathology." Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc vol. 34,12 (2021): 2098-2108. doi: https://doi.org/10.1038/s41379-021-00859-x
Schömig-Markiefka B, Pryalukhin A, Hulla W, Bychkov A, Fukuoka J, Madabhushi A, Achter V, Nieroda L, Büttner R, Quaas A, Tolkach Y. Quality control stress test for deep learning-based diagnostic model in digital pathology. Mod Pathol. 2021 Dec;34(12):2098-2108. doi: 10.1038/s41379-021-00859-x. Epub 2021 Jun 24. PMID: 34168282; PMCID: PMC8592835.
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Multi-Compartment Spatially-Derived Radiomics From Optical Coherence Tomography Predict Anti-VEGF Treatment Durability in Macular Edema Secondary to Retinal Vascular Disease: Preliminary Findings.

IEEE journal of translational engineering in health and medicine

Sil Kar S, Sevgi DD, Dong V, Srivastava SK, Madabhushi A, Ehlers JP.
PMID: 34350068
IEEE J Transl Eng Health Med. 2021 Jul 12;9:1000113. doi: 10.1109/JTEHM.2021.3096378. eCollection 2021.

OBJECTIVE: Diabetic macular edema (DME) and retinal vein occlusion (RVO) are the leading causes of visual impairments across the world. Vascular endothelial growth factor (VEGF) stimulates breakdown of blood-retinal barrier that causes accumulation of fluid within macula. Anti-VEGF therapy...

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Sil Kar S, Sevgi DD, Dong V, et al. Multi-Compartment Spatially-Derived Radiomics From Optical Coherence Tomography Predict Anti-VEGF Treatment Durability in Macular Edema Secondary to Retinal Vascular Disease: Preliminary Findings. IEEE J Transl Eng Health Med. 2021;9:1000113doi: 10.1109/JTEHM.2021.3096378.
Sil Kar, S., Sevgi, D. D., Dong, V., Srivastava, S. K., Madabhushi, A., & Ehlers, J. P. (2021). Multi-Compartment Spatially-Derived Radiomics From Optical Coherence Tomography Predict Anti-VEGF Treatment Durability in Macular Edema Secondary to Retinal Vascular Disease: Preliminary Findings. IEEE journal of translational engineering in health and medicine, 91000113. https://doi.org/10.1109/JTEHM.2021.3096378
Sil Kar, Sudeshna, et al. "Multi-Compartment Spatially-Derived Radiomics From Optical Coherence Tomography Predict Anti-VEGF Treatment Durability in Macular Edema Secondary to Retinal Vascular Disease: Preliminary Findings." IEEE journal of translational engineering in health and medicine vol. 9 (2021): 1000113. doi: https://doi.org/10.1109/JTEHM.2021.3096378
Sil Kar S, Sevgi DD, Dong V, Srivastava SK, Madabhushi A, Ehlers JP. Multi-Compartment Spatially-Derived Radiomics From Optical Coherence Tomography Predict Anti-VEGF Treatment Durability in Macular Edema Secondary to Retinal Vascular Disease: Preliminary Findings. IEEE J Transl Eng Health Med. 2021 Jul 12;9:1000113. doi: 10.1109/JTEHM.2021.3096378. eCollection 2021. PMID: 34350068; PMCID: PMC8328398.
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A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT.

BMC medical imaging

Atta-Fosu T, LaBarbera M, Ghose S, Schoenhagen P, Saliba W, Tchou PJ, Lindsay BD, Desai MY, Kwon D, Chung MK, Madabhushi A.
PMID: 33750343
BMC Med Imaging. 2021 Mar 09;21(1):45. doi: 10.1186/s12880-021-00578-4.

OBJECTIVE: To investigate left atrial shape differences on CT scans of atrial fibrillation (AF) patients with (AF+) versus without (AF-) post-ablation recurrence and whether these shape differences predict AF recurrence.METHODS: This retrospective study included 68 AF patients who had...

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Atta-Fosu T, LaBarbera M, Ghose S, et al. A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT. BMC Med Imaging. 2021;21(1):45doi: 10.1186/s12880-021-00578-4.
Atta-Fosu, T., LaBarbera, M., Ghose, S., Schoenhagen, P., Saliba, W., Tchou, P. J., Lindsay, B. D., Desai, M. Y., Kwon, D., Chung, M. K., & Madabhushi, A. (2021). A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT. BMC medical imaging, 21(1), 45. https://doi.org/10.1186/s12880-021-00578-4
Atta-Fosu, Thomas, et al. "A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT." BMC medical imaging vol. 21,1 (2021): 45. doi: https://doi.org/10.1186/s12880-021-00578-4
Atta-Fosu T, LaBarbera M, Ghose S, Schoenhagen P, Saliba W, Tchou PJ, Lindsay BD, Desai MY, Kwon D, Chung MK, Madabhushi A. A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT. BMC Med Imaging. 2021 Mar 09;21(1):45. doi: 10.1186/s12880-021-00578-4. PMID: 33750343; PMCID: PMC7941998.
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Quality control stress test for deep learning-based diagnostic model in digital pathology.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc

Schömig-Markiefka B, Pryalukhin A, Hulla W, Bychkov A, Fukuoka J, Madabhushi A, Achter V, Nieroda L, Büttner R, Quaas A, Tolkach Y.
PMID: 34168282
Mod Pathol. 2021 Dec;34(12):2098-2108. doi: 10.1038/s41379-021-00859-x. Epub 2021 Jun 24.

Digital pathology provides a possibility for computational analysis of histological slides and automatization of routine pathological tasks. Histological slides are very heterogeneous concerning staining, sections' thickness, and artifacts arising during tissue processing, cutting, staining, and digitization. In this study,...

Cite
Schömig-Markiefka B, Pryalukhin A, Hulla W, et al. Quality control stress test for deep learning-based diagnostic model in digital pathology. Mod Pathol. 2021;34(12):2098-2108doi: 10.1038/s41379-021-00859-x.
Schömig-Markiefka, B., Pryalukhin, A., Hulla, W., Bychkov, A., Fukuoka, J., Madabhushi, A., Achter, V., Nieroda, L., Büttner, R., Quaas, A., & Tolkach, Y. (2021). Quality control stress test for deep learning-based diagnostic model in digital pathology. Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc, 34(12), 2098-2108. https://doi.org/10.1038/s41379-021-00859-x
Schömig-Markiefka, Birgid, et al. "Quality control stress test for deep learning-based diagnostic model in digital pathology." Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc vol. 34,12 (2021): 2098-2108. doi: https://doi.org/10.1038/s41379-021-00859-x
Schömig-Markiefka B, Pryalukhin A, Hulla W, Bychkov A, Fukuoka J, Madabhushi A, Achter V, Nieroda L, Büttner R, Quaas A, Tolkach Y. Quality control stress test for deep learning-based diagnostic model in digital pathology. Mod Pathol. 2021 Dec;34(12):2098-2108. doi: 10.1038/s41379-021-00859-x. Epub 2021 Jun 24. PMID: 34168282; PMCID: PMC8592835.
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Showing 1 to 12 of 14 entries