The History of Paige

History Timeline of Paige.


Our Two Primary Goals


1
Develop and deliver a series of AI disease modules that allow pathologists to improve the scalability of their work, enabling them to provide better care, at lower cost.
2
Develop and introduce new treatment paradigms, that build on the promise of computational pathology, and integrate computational pathology with EHR, genomic and other data.


Our Technology


We are building an Artificial Intelligence to fundamentally change clinical diagnosis and treatment of cancer. We are working on general and organ specific modules which allow PAIGE to fulfill a plethora of tasks ranging from rapid stratification to tumor detection and segmentation as well as prediction of treatment response and survival.


Deep Neural Network

At the heart of PAIGE are large-scale Machine Learning algorithms that are trained at petabyte-scale from tens of thousands of digital slides. We are developing novel deep learning algorithms based on convolutional and recurrent neural networks as well as generative models that are able to learn efficiently from an unprecedented wealth of visual and clinical data.


Our Leading Product


Smart AI modules are only useful to patients and doctors if they are implemented and used in the clinic.


PAIGE's slide viewer

Our leading product is an application suite with a novel slide viewer which is microscope vendor agnostic, device independent and the fastest viewer in the field. In addition to delivering PAIGE's AI modules to the pathologist, it is fully integrated into the laboratory information systems, allowing for a seamless application in the clinical workflow.

In 2017 PAIGE's slide viewer was rolled out institution-wide at Memorial Sloan Kettering Cancer Center and is the single entry point for pathologists and cancer researchers. We are developing a series of disease-specific modules, which will be rolled out starting later in 2018.



Selected Publications


See also our complete publication list.

Computational Pathology Analysis of Tissue Microarrays Predicts Survival of Renal Clear Cell Carcinoma Patients.
Thomas J. Fuchs, Peter J. Wild, Holger Moch and Joachim M. Buhmann.
Proceedings of the international conference on Medical Image Computing and Computer-Assisted Intervention MICCAI, vol. 5242, p. 1-8, Lecture Notes in Computer Science, Springer-Verlag, ISBN 978-3-540-85989-5, 2008
PDF   URL   BibTeX   Endnote / RIS   Abstract
Download BibTeX citation
@inproceedings{fuchs_computational_2008,
    address = {Berlin, Heidelberg},
    series = {Lecture {Notes} in {Computer} {Science}},
    title = {Computational {Pathology} {Analysis} of {Tissue} {Microarrays} {Predicts} {Survival} of {Renal} {Clear} {Cell} {Carcinoma} {Patients}},
    volume = {5242},
    isbn = {978-3-540-85989-5},
    url = {http://dx.doi.org/10.1007/978-3-540-85990-1_1},
    doi = {10.1007/978-3-540-85990-1_1},
    booktitle = {Proceedings of the international conference on {Medical} {Image} {Computing} and {Computer}-{Assisted} {Intervention} {MICCAI}},
    publisher = {Springer-Verlag},
    author = {Fuchs, Thomas J. and Wild, Peter J. and Moch, Holger and Buhmann, Joachim M.},
    year = {2008},
    keywords = {Computer Science},
    pages = {1--8}
}
Download Endnote/RIS citation
TY - CONF
TI - Computational Pathology Analysis of Tissue Microarrays Predicts Survival of Renal Clear Cell Carcinoma Patients
AU - Fuchs, Thomas J.
AU - Wild, Peter J.
AU - Moch, Holger
AU - Buhmann, Joachim M.
T3 - Lecture Notes in Computer Science
C1 - Berlin, Heidelberg
C3 - Proceedings of the international conference on Medical Image Computing and Computer-Assisted Intervention MICCAI
DA - 2008///
PY - 2008
DO - 10.1007/978-3-540-85990-1_1
VL - 5242
SP - 1
EP - 8
PB - Springer-Verlag
SN - 978-3-540-85989-5
UR - http://dx.doi.org/10.1007/978-3-540-85990-1_1
KW - Computer Science
ER -
Computational Pathology: Challenges and Promises for Tissue Analysis.
Thomas J. Fuchs and Joachim M. Buhmann.
Computerized Medical Imaging and Graphics, vol. 35, 7–8, p. 515-530, 2011
PDF   URL   BibTeX   Endnote / RIS   Abstract
Download BibTeX citation
@article{fuchs_computational_2011,
    title = {Computational {Pathology}: {Challenges} and {Promises} for {Tissue} {Analysis}},
    volume = {35},
    issn = {0895-6111},
    shorttitle = {Computational pathology},
    url = {http://www.sciencedirect.com/science/article/pii/S0895611111000383},
    doi = {10.1016/j.compmedimag.2011.02.006},
    abstract = {The histological assessment of human tissue has emerged as the key challenge for detection and treatment of cancer. A plethora of different data sources ranging from tissue microarray data to gene expression, proteomics or metabolomics data provide a detailed overview of the health status of a patient. Medical doctors need to assess these information sources and they rely on data driven automatic analysis tools. Methods for classification, grouping and segmentation of heterogeneous data sources as well as regression of noisy dependencies and estimation of survival probabilities enter the processing workflow of a pathology diagnosis system at various stages. This paper reports on state-of-the-art of the design and effectiveness of computational pathology workflows and it discusses future research directions in this emergent field of medical informatics and diagnostic machine learning.},
    number = {7–8},
    urldate = {2012-03-08TZ},
    journal = {Computerized Medical Imaging and Graphics},
    author = {Fuchs, Thomas J. and Buhmann, Joachim M.},
    year = {2011},
    keywords = {Cancer research, Computational pathology, Medical imaging, Survival statistics, Whole, Whole slide imaging, imaging, machine learning, slide},
    pages = {515--530}
}
Download Endnote/RIS citation
TY - JOUR
TI - Computational Pathology: Challenges and Promises for Tissue Analysis
AU - Fuchs, Thomas J.
AU - Buhmann, Joachim M.
T2 - Computerized Medical Imaging and Graphics
AB - The histological assessment of human tissue has emerged as the key challenge for detection and treatment of cancer. A plethora of different data sources ranging from tissue microarray data to gene expression, proteomics or metabolomics data provide a detailed overview of the health status of a patient. Medical doctors need to assess these information sources and they rely on data driven automatic analysis tools. Methods for classification, grouping and segmentation of heterogeneous data sources as well as regression of noisy dependencies and estimation of survival probabilities enter the processing workflow of a pathology diagnosis system at various stages. This paper reports on state-of-the-art of the design and effectiveness of computational pathology workflows and it discusses future research directions in this emergent field of medical informatics and diagnostic machine learning.
DA - 2011///
PY - 2011
DO - 10.1016/j.compmedimag.2011.02.006
DP - ScienceDirect
VL - 35
IS - 7–8
SP - 515
EP - 530
SN - 0895-6111
ST - Computational pathology
UR - http://www.sciencedirect.com/science/article/pii/S0895611111000383
Y2 - 2012/03/08/T15:27:48Z
KW - Cancer research
KW - Computational pathology
KW - Medical imaging
KW - Survival statistics
KW - Whole
KW - Whole slide imaging
KW - imaging
KW - machine learning
KW - slide
ER -
The histological assessment of human tissue has emerged as the key challenge for detection and treatment of cancer. A plethora of different data sources ranging from tissue microarray data to gene expression, proteomics or metabolomics data provide a detailed overview of the health status of a patient. Medical doctors need to assess these information sources and they rely on data driven automatic analysis tools. Methods for classification, grouping and segmentation of heterogeneous data sources as well as regression of noisy dependencies and estimation of survival probabilities enter the processing workflow of a pathology diagnosis system at various stages. This paper reports on state-of-the-art of the design and effectiveness of computational pathology workflows and it discusses future research directions in this emergent field of medical informatics and diagnostic machine learning.
Mitochondria-based Renal Cell Carcinoma Subtyping: Learning from Deep vs. Flat Feature Representations.
Peter J. Schüffler, Judy Sarungbam, Hassan Muhammad, Ed Reznik, Satish K. Tickoo and Thomas J. Fuchs.
Proceedings of the 1st Machine Learning for Healthcare Conference, Machine Learning for Healthcare, vol. 56, p. 191-208, Proceedings of Machine Learning Research, PMLR, 2016
PDF   URL   BibTeX   Endnote / RIS   Abstract
Download BibTeX citation
@inproceedings{schuffler_mitochondria-based_2016,
    address = {Los Angeles},
    series = {Proceedings of {Machine} {Learning} {Research}},
    title = {Mitochondria-based {Renal} {Cell} {Carcinoma} {Subtyping}: {Learning} from {Deep} vs. {Flat} {Feature} {Representations}},
    volume = {56},
    url = {http://proceedings.mlr.press/v56/Schuffler16.html},
    abstract = {Accurate subtyping of renal cell carcinoma (RCC) is of crucial importance for understanding disease progression and for making informed treatment decisions. New discoveries of significant mitochondria alterations between subtypes makes immunohistochemical (IHC) staining based image classification an imperative.
    Until now, accurate quantification and subtyping was made impossible by huge IHC variations, the absence of cell membrane staining for cytoplasm segmentation as well as the complete lack of systems for robust and reproducible image based classification.
    In this paper we present a comprehensive classification framework to overcome these challenges for tissue microarrays (TMA) of RCCs. We compare and evaluate models based on domain specific hand-crafted "flat"-features versus "deep" feature representations from various layers of a pre-trained convolutional neural network (CNN).
    The best model reaches a cross-validation accuracy of 89\%, which demonstrates for the first time, that robust mitochondria-based subtyping of renal cancer is feasible.},
    language = {English},
    booktitle = {Proceedings of the 1st {Machine} {Learning} for {Healthcare} {Conference}},
    publisher = {PMLR},
    author = {Sch\"uffler, Peter J. and Sarungbam, Judy and Muhammad, Hassan and Reznik, Ed and Tickoo, Satish K. and Fuchs, Thomas J.},
    editor = {Finale, Doshi-Valez and Fackler, Jim and Kale, David and Wallace, Byron and Weins, Jenna},
    month = aug,
    year = {2016},
    pages = {191--208}
}
Download Endnote/RIS citation
TY - CONF
TI - Mitochondria-based Renal Cell Carcinoma Subtyping: Learning from Deep vs. Flat Feature Representations
AU - Schüffler, Peter J.
AU - Sarungbam, Judy
AU - Muhammad, Hassan
AU - Reznik, Ed
AU - Tickoo, Satish K.
AU - Fuchs, Thomas J.
T2 - Machine Learning for Healthcare
A2 - Finale, Doshi-Valez
A2 - Fackler, Jim
A2 - Kale, David
A2 - Wallace, Byron
A2 - Weins, Jenna
T3 - Proceedings of Machine Learning Research
AB - Accurate subtyping of renal cell carcinoma (RCC) is of crucial importance for understanding disease progression and for making informed treatment decisions. New discoveries of significant mitochondria alterations between subtypes makes immunohistochemical (IHC) staining based image classification an imperative.
Until now, accurate quantification and subtyping was made impossible by huge IHC variations, the absence of cell membrane staining for cytoplasm segmentation as well as the complete lack of systems for robust and reproducible image based classification.
In this paper we present a comprehensive classification framework to overcome these challenges for tissue microarrays (TMA) of RCCs. We compare and evaluate models based on domain specific hand-crafted "flat"-features versus "deep" feature representations from various layers of a pre-trained convolutional neural network (CNN).
The best model reaches a cross-validation accuracy of 89%, which demonstrates for the first time, that robust mitochondria-based subtyping of renal cancer is feasible.
C1 - Los Angeles
C3 - Proceedings of the 1st Machine Learning for Healthcare Conference
DA - 2016/08/18/
PY - 2016
VL - 56
SP - 191
EP - 208
LA - English
PB - PMLR
UR - http://proceedings.mlr.press/v56/Schuffler16.html
ER -
Accurate subtyping of renal cell carcinoma (RCC) is of crucial importance for understanding disease progression and for making informed treatment decisions. New discoveries of significant mitochondria alterations between subtypes makes immunohistochemical (IHC) staining based image classification an imperative. Until now, accurate quantification and subtyping was made impossible by huge IHC variations, the absence of cell membrane staining for cytoplasm segmentation as well as the complete lack of systems for robust and reproducible image based classification. In this paper we present a comprehensive classification framework to overcome these challenges for tissue microarrays (TMA) of RCCs. We compare and evaluate models based on domain specific hand-crafted "flat"-features versus "deep" feature representations from various layers of a pre-trained convolutional neural network (CNN). The best model reaches a cross-validation accuracy of 89%, which demonstrates for the first time, that robust mitochondria-based subtyping of renal cancer is feasible.
Computational Pathology.
Peter J. Schüffler, Qing Zhong, Peter J. Wild and Thomas J. Fuchs.
In: Johannes Haybäck (ed.) Mechanisms of Molecular Carcinogenesis - Volume 2, 1st ed. 2017 edition, Springer, ISBN 3-319-53660-5, 21. Jun. 2017
PDF    URL   BibTeX   Endnote / RIS   Abstract
Download BibTeX citation
@incollection{schuffler_computational_2017,
    edition = {1st ed. 2017 edition},
    title = {Computational {Pathology}},
    isbn = {3-319-53660-5},
    url = {http://www.springer.com/de/book/9783319536606},
    booktitle = {Mechanisms of {Molecular} {Carcinogenesis} - {Volume} 2},
    publisher = {Springer},
    author = {Sch\"uffler, Peter J. and Zhong, Qing and Wild, Peter J. and Fuchs, Thomas J.},
    editor = {Hayb\"ack, Johannes},
    month = jun,
    year = {2017}
}
Download Endnote/RIS citation
TY - CHAP
TI - Computational Pathology
AU - Schüffler, Peter J.
AU - Zhong, Qing
AU - Wild, Peter J.
AU - Fuchs, Thomas J.
T2 - Mechanisms of Molecular Carcinogenesis - Volume 2
A2 - Haybäck, Johannes
DA - 2017/06/21/
PY - 2017
ET - 1st ed. 2017 edition
PB - Springer
SN - 3-319-53660-5
UR - http://www.springer.com/de/book/9783319536606
ER -

See also our complete publication list.
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