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Showing 1 to 7 of 7 entries
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Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images.

Pattern recognition

Hou L, Nguyen V, Kanevsky AB, Samaras D, Kurc TM, Zhao T, Gupta RR, Gao Y, Chen W, Foran D, Saltz JH.
PMID: 30631215
Pattern Recognit. 2019 Feb;86:188-200. doi: 10.1016/j.patcog.2018.09.007. Epub 2018 Sep 13.

We propose a sparse Convolutional Autoencoder (CAE) for simultaneous nucleus detection and feature extraction in histopathology tissue images. Our CAE detects and encodes nuclei in image patches in tissue images into sparse feature maps that encode both the location...

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Hou L, Nguyen V, Kanevsky AB, et al. Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images. Pattern Recognit. 2018;86:188-200doi: 10.1016/j.patcog.2018.09.007.
Hou, L., Nguyen, V., Kanevsky, A. B., Samaras, D., Kurc, T. M., Zhao, T., Gupta, R. R., Gao, Y., Chen, W., Foran, D., & Saltz, J. H. (2019). Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images. Pattern recognition, 86188-200. https://doi.org/10.1016/j.patcog.2018.09.007
Hou, Le, et al. "Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images." Pattern recognition vol. 86 (2019): 188-200. doi: https://doi.org/10.1016/j.patcog.2018.09.007
Hou L, Nguyen V, Kanevsky AB, Samaras D, Kurc TM, Zhao T, Gupta RR, Gao Y, Chen W, Foran D, Saltz JH. Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images. Pattern Recognit. 2019 Feb;86:188-200. doi: 10.1016/j.patcog.2018.09.007. Epub 2018 Sep 13. PMID: 30631215; PMCID: PMC6322841.
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[No title available]

IEEE access : practical innovations, open solutions

Saha M, Guo X, Sharma A.
PMID: 34178560
IEEE Access. 2021;9:79829-79840. doi: 10.1109/access.2021.3084597. Epub 2021 May 28.

Tumor-infiltrating lymphocytes (TILs) act as immune cells against cancer tissues. The manual assessment of TILs is usually erroneous, tedious, costly and subject to inter- and intraobserver variability. Machine learning approaches can solve these issues, but they require a large...

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Saha M, Guo X, Sharma A. . IEEE Access. 2021;9:79829-79840doi: 10.1109/access.2021.3084597.
Saha, M., Guo, X., & Sharma, A. (2021). . IEEE access : practical innovations, open solutions, 979829-79840. https://doi.org/10.1109/access.2021.3084597
Saha, Monjoy, et al. "." IEEE access : practical innovations, open solutions vol. 9 (2021): 79829-79840. doi: https://doi.org/10.1109/access.2021.3084597
Saha M, Guo X, Sharma A. . IEEE Access. 2021;9:79829-79840. doi: 10.1109/access.2021.3084597. Epub 2021 May 28. PMID: 34178560; PMCID: PMC8224465.
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Twenty Years of Digital Pathology: An Overview of the Road Travelled, What is on the Horizon, and the Emergence of Vendor-Neutral Archives.

Journal of pathology informatics

Pantanowitz L, Sharma A, Carter AB, Kurc T, Sussman A, Saltz J.
PMID: 30607307
J Pathol Inform. 2018 Nov 21;9:40. doi: 10.4103/jpi.jpi_69_18. eCollection 2018.

Almost 20 years have passed since the commercial introduction of whole-slide imaging (WSI) scanners. During this time, the creation of various WSI devices with the ability to digitize an entire glass slide has transformed the field of pathology. Parallel...

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Pantanowitz L, Sharma A, Carter AB, et al. Twenty Years of Digital Pathology: An Overview of the Road Travelled, What is on the Horizon, and the Emergence of Vendor-Neutral Archives. J Pathol Inform. 2018;9:40doi: 10.4103/jpi.jpi_69_18.
Pantanowitz, L., Sharma, A., Carter, A. B., Kurc, T., Sussman, A., & Saltz, J. (2018). Twenty Years of Digital Pathology: An Overview of the Road Travelled, What is on the Horizon, and the Emergence of Vendor-Neutral Archives. Journal of pathology informatics, 940. https://doi.org/10.4103/jpi.jpi_69_18
Pantanowitz, Liron, et al. "Twenty Years of Digital Pathology: An Overview of the Road Travelled, What is on the Horizon, and the Emergence of Vendor-Neutral Archives." Journal of pathology informatics vol. 9 (2018): 40. doi: https://doi.org/10.4103/jpi.jpi_69_18
Pantanowitz L, Sharma A, Carter AB, Kurc T, Sussman A, Saltz J. Twenty Years of Digital Pathology: An Overview of the Road Travelled, What is on the Horizon, and the Emergence of Vendor-Neutral Archives. J Pathol Inform. 2018 Nov 21;9:40. doi: 10.4103/jpi.jpi_69_18. eCollection 2018. PMID: 30607307; PMCID: PMC6289005.
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Comparison of Different Classifiers with Active Learning to Support Quality Control in Nucleus Segmentation in Pathology Images.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science

Wen S, Kurc TM, Hou L, Saltz JH, Gupta RR, Batiste R, Zhao T, Nguyen V, Samaras D, Zhu W.
PMID: 29888078
AMIA Jt Summits Transl Sci Proc. 2018 May 18;2017:227-236. eCollection 2018.

Segmentation of nuclei in whole slide tissue images is a common methodology in pathology image analysis. Most segmentation algorithms are sensitive to input algorithm parameters and the characteristics of input images (tissue morphology, staining, etc.). Because there can be...

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Wen S, Kurc TM, Hou L, et al. Comparison of Different Classifiers with Active Learning to Support Quality Control in Nucleus Segmentation in Pathology Images. AMIA Jt Summits Transl Sci Proc. 2018;2017:227-236
Wen, S., Kurc, T. M., Hou, L., Saltz, J. H., Gupta, R. R., Batiste, R., Zhao, T., Nguyen, V., Samaras, D., & Zhu, W. (2018). Comparison of Different Classifiers with Active Learning to Support Quality Control in Nucleus Segmentation in Pathology Images. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2017227-236.
Wen, Si, et al. "Comparison of Different Classifiers with Active Learning to Support Quality Control in Nucleus Segmentation in Pathology Images." AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science vol. 2017 (2018): 227-236.
Wen S, Kurc TM, Hou L, Saltz JH, Gupta RR, Batiste R, Zhao T, Nguyen V, Samaras D, Zhu W. Comparison of Different Classifiers with Active Learning to Support Quality Control in Nucleus Segmentation in Pathology Images. AMIA Jt Summits Transl Sci Proc. 2018 May 18;2017:227-236. eCollection 2018. PMID: 29888078; PMCID: PMC5961826.
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Parallel and Efficient Sensitivity Analysis of Microscopy Image Segmentation Workflows in Hybrid Systems.

Proceedings. IEEE International Conference on Cluster Computing

Barreiros W, Teodoro G, Kurc T, Kong J, Melo ACMA, Saltz J.
PMID: 29081725
Proc IEEE Int Conf Clust Comput. 2017 Sep;2017:25-35. doi: 10.1109/CLUSTER.2017.28. Epub 2017 Sep 26.

We investigate efficient sensitivity analysis (SA) of algorithms that segment and classify image features in a large dataset of high-resolution images. Algorithm SA is the process of evaluating variations of methods and parameter values to quantify differences in the...

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Barreiros W, Teodoro G, Kurc T, et al. Parallel and Efficient Sensitivity Analysis of Microscopy Image Segmentation Workflows in Hybrid Systems. Proc IEEE Int Conf Clust Comput. 2017;2017:25-35doi: 10.1109/CLUSTER.2017.28.
Barreiros, W., Teodoro, G., Kurc, T., Kong, J., Melo, A. C. M. A., & Saltz, J. (2017). Parallel and Efficient Sensitivity Analysis of Microscopy Image Segmentation Workflows in Hybrid Systems. Proceedings. IEEE International Conference on Cluster Computing, 201725-35. https://doi.org/10.1109/CLUSTER.2017.28
Barreiros, Willian, et al. "Parallel and Efficient Sensitivity Analysis of Microscopy Image Segmentation Workflows in Hybrid Systems." Proceedings. IEEE International Conference on Cluster Computing vol. 2017 (2017): 25-35. doi: https://doi.org/10.1109/CLUSTER.2017.28
Barreiros W, Teodoro G, Kurc T, Kong J, Melo ACMA, Saltz J. Parallel and Efficient Sensitivity Analysis of Microscopy Image Segmentation Workflows in Hybrid Systems. Proc IEEE Int Conf Clust Comput. 2017 Sep;2017:25-35. doi: 10.1109/CLUSTER.2017.28. Epub 2017 Sep 26. PMID: 29081725; PMCID: PMC5658136.
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Robust Histopathology Image Analysis: to Label or to Synthesize?.

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Hou L, Agarwal A, Samaras D, Kurc TM, Gupta RR, Saltz JH.
PMID: 34025103
Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2019 Jun;2019:8533-8542. doi: 10.1109/CVPR.2019.00873. Epub 2020 Jan 09.

Detection, segmentation and classification of nuclei are fundamental analysis operations in digital pathology. Existing state-of-the-art approaches demand extensive amount of supervised training data from pathologists and may still perform poorly in images from unseen tissue types. We propose an...

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Hou L, Agarwal A, Samaras D, et al. Robust Histopathology Image Analysis: to Label or to Synthesize?. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2020;2019:8533-8542doi: 10.1109/CVPR.2019.00873.
Hou, L., Agarwal, A., Samaras, D., Kurc, T. M., Gupta, R. R., & Saltz, J. H. (2019). Robust Histopathology Image Analysis: to Label or to Synthesize?. Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 20198533-8542. https://doi.org/10.1109/CVPR.2019.00873
Hou, Le, et al. "Robust Histopathology Image Analysis: to Label or to Synthesize?." Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition vol. 2019 (2019): 8533-8542. doi: https://doi.org/10.1109/CVPR.2019.00873
Hou L, Agarwal A, Samaras D, Kurc TM, Gupta RR, Saltz JH. Robust Histopathology Image Analysis: to Label or to Synthesize?. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2019 Jun;2019:8533-8542. doi: 10.1109/CVPR.2019.00873. Epub 2020 Jan 09. PMID: 34025103; PMCID: PMC8139403.
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Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches.

Frontiers in neuroscience

Kurc T, Bakas S, Ren X, Bagari A, Momeni A, Huang Y, Zhang L, Kumar A, Thibault M, Qi Q, Wang Q, Kori A, Gevaert O, Zhang Y, Shen D, Khened M, Ding X, Krishnamurthi G, Kalpathy-Cramer J, Davis J, Zhao T, Gupta R, Saltz J, Farahani K.
PMID: 32153349
Front Neurosci. 2020 Feb 21;14:27. doi: 10.3389/fnins.2020.00027. eCollection 2020.

Biomedical imaging Is an important source of information in cancer research. Characterizations of cancer morphology at onset, progression, and in response to treatment provide complementary information to that gleaned from genomics and clinical data. Accurate extraction and classification of...

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Kurc T, Bakas S, Ren X, et al. Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches. Front Neurosci. 2020;14:27doi: 10.3389/fnins.2020.00027.
Kurc, T., Bakas, S., Ren, X., Bagari, A., Momeni, A., Huang, Y., Zhang, L., Kumar, A., Thibault, M., Qi, Q., Wang, Q., Kori, A., Gevaert, O., Zhang, Y., Shen, D., Khened, M., Ding, X., Krishnamurthi, G., Kalpathy-Cramer, J., Davis, J., Zhao, T., Gupta, R., Saltz, J., & Farahani, K. (2020). Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches. Frontiers in neuroscience, 1427. https://doi.org/10.3389/fnins.2020.00027
Kurc, Tahsin, et al. "Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches." Frontiers in neuroscience vol. 14 (2020): 27. doi: https://doi.org/10.3389/fnins.2020.00027
Kurc T, Bakas S, Ren X, Bagari A, Momeni A, Huang Y, Zhang L, Kumar A, Thibault M, Qi Q, Wang Q, Kori A, Gevaert O, Zhang Y, Shen D, Khened M, Ding X, Krishnamurthi G, Kalpathy-Cramer J, Davis J, Zhao T, Gupta R, Saltz J, Farahani K. Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches. Front Neurosci. 2020 Feb 21;14:27. doi: 10.3389/fnins.2020.00027. eCollection 2020. PMID: 32153349; PMCID: PMC7046596.
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Showing 1 to 7 of 7 entries