#computationalpathology — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #computationalpathology, aggregated by home.social.
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https://www.europesays.com/ch/69821/ AstraZeneca Collaborates with Roche Diagnostics Asia Pacific to Help Accelerate Sustainable Ecosystem for Advanced Pathology in Breast and Lung Cancer #AsiaPacific #AstraZeneca #BreastCancer #ComputationalPathology #DiagnosticAccuracy #DigitalPathology #LungCancer #Roche #RocheDiagnostics
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https://www.europesays.com/ch/67229/ AstraZeneca Collaborates with Roche Diagnostics Asia Pacific to Help Accelerate Sustainable Ecosystem for Advanced Pathology in Breast and Lung Cancer #AsiaPacific #AstraZeneca #BreastCancer #ComputationalPathology #DiagnosticAccuracy #DigitalPathology #LungCancer #Roche #RocheDiagnostics
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We’re looking for a way to version and catalogue self-trained deep learning models (training data, code revision, etc.) from our Tissue-Concepts family of medical foundation models.
We’ve briefly looked at #W&B, #MLflow (now integrated into GitLab), and intensely tried storing more-or-less-documented model snapshots to disk.
Has anyone had good or bad experiences with these tools in research / medical ML settings? Any recommendations?
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We’re looking for a way to version and catalogue self-trained deep learning models (training data, code revision, etc.) from our Tissue-Concepts family of medical foundation models.
We’ve briefly looked at #W&B, #MLflow (now integrated into GitLab), and intensely tried storing more-or-less-documented model snapshots to disk.
Has anyone had good or bad experiences with these tools in research / medical ML settings? Any recommendations?
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We’re looking for a way to version and catalogue self-trained deep learning models (training data, code revision, etc.) from our Tissue-Concepts family of medical foundation models.
We’ve briefly looked at #W&B, #MLflow (now integrated into GitLab), and intensely tried storing more-or-less-documented model snapshots to disk.
Has anyone had good or bad experiences with these tools in research / medical ML settings? Any recommendations?
-
We’re looking for a way to version and catalogue self-trained deep learning models (training data, code revision, etc.) from our Tissue-Concepts family of medical foundation models.
We’ve briefly looked at #W&B, #MLflow (now integrated into GitLab), and intensely tried storing more-or-less-documented model snapshots to disk.
Has anyone had good or bad experiences with these tools in research / medical ML settings? Any recommendations?
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Project announcement 🏗️: "PROSurvival" builds a collaborative federated learning framework to predict survival in prostate cancer patients.
Partners are OFFIS - Institute for Information Technology, Charité, Goethe University Frankfurt, and Fraunhofer MEVIS.
Here's the gist: In the long run, we want to find predictive image features that can be identified in tissue sections from routine diagnostics.
#federatedlearning #ai #foundationmodels #ComputationalPathology
1/3
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Project announcement 🏗️: "PROSurvival" builds a collaborative federated learning framework to predict survival in prostate cancer patients.
Partners are OFFIS - Institute for Information Technology, Charité, Goethe University Frankfurt, and Fraunhofer MEVIS.
Here's the gist: In the long run, we want to find predictive image features that can be identified in tissue sections from routine diagnostics.
#federatedlearning #ai #foundationmodels #ComputationalPathology
1/3
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Project announcement 🏗️: "PROSurvival" builds a collaborative federated learning framework to predict survival in prostate cancer patients.
Partners are OFFIS - Institute for Information Technology, Charité, Goethe University Frankfurt, and Fraunhofer MEVIS.
Here's the gist: In the long run, we want to find predictive image features that can be identified in tissue sections from routine diagnostics.
#federatedlearning #ai #foundationmodels #ComputationalPathology
1/3
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Project announcement 🏗️: "PROSurvival" builds a collaborative federated learning framework to predict survival in prostate cancer patients.
Partners are OFFIS - Institute for Information Technology, Charité, Goethe University Frankfurt, and Fraunhofer MEVIS.
Here's the gist: In the long run, we want to find predictive image features that can be identified in tissue sections from routine diagnostics.
#federatedlearning #ai #foundationmodels #ComputationalPathology
1/3
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Project announcement 🏗️: "PROSurvival" builds a collaborative federated learning framework to predict survival in prostate cancer patients.
Partners are OFFIS - Institute for Information Technology, Charité, Goethe University Frankfurt, and Fraunhofer MEVIS.
Here's the gist: In the long run, we want to find predictive image features that can be identified in tissue sections from routine diagnostics.
#federatedlearning #ai #foundationmodels #ComputationalPathology
1/3
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If you are at SPIE #medicalimaging in SanDiego, check Monika Pytlarz poster on classification of #brain #tumor biopsies TMA via #deeplearning, a work with
@NenckiInstitute. She is up at 6-8 pm (California time)
#glioma #histology #computationalpathology #pathology -
If you are at SPIE #medicalimaging in SanDiego, check Monika Pytlarz poster on classification of #brain #tumor biopsies TMA via #deeplearning, a work with
@NenckiInstitute. She is up at 6-8 pm (California time)
#glioma #histology #computationalpathology #pathology -
If you are at SPIE #medicalimaging in SanDiego, check Monika Pytlarz poster on classification of #brain #tumor biopsies TMA via #deeplearning, a work with
@NenckiInstitute. She is up at 6-8 pm (California time)
#glioma #histology #computationalpathology #pathology -
If you are at SPIE #medicalimaging in SanDiego, check Monika Pytlarz poster on classification of #brain #tumor biopsies TMA via #deeplearning, a work with
@NenckiInstitute. She is up at 6-8 pm (California time)
#glioma #histology #computationalpathology #pathology -
If you are at SPIE #medicalimaging in SanDiego, check Monika Pytlarz poster on classification of #brain #tumor biopsies TMA via #deeplearning, a work with
@NenckiInstitute. She is up at 6-8 pm (California time)
#glioma #histology #computationalpathology #pathology