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Project:
MOSSAIC
Description:
Offers a hierarchical self-attention network for information extraction from long (more than 400 words) cancer pathology reports.
DESCRIPTION:
Offers a hierarchical self-attention network for information extraction from long (more than 400 words) cancer pathology reports.
IMPACT: Allows automatic information extraction from free-form pathology report texts. More accurate than MT-CNN.
PRIMARY PUBLICATION: Limitations of Transformers on Clinical Text Classification
INPUT DATA FORMAT: Unspecified
LEVEL OF DOCUMENTATION: Minimal
AVAILABLE ON GITHUB
Project:
MOSSAIC
Description:
Offers a convolutional neural network for natural language processing and information extraction from free-form texts.
DESCRIPTION:
Offers a convolutional neural network for natural language processing and information extraction from free-form texts.
IMPACT: Allows automatic information extraction from free-form pathology report texts. Faster than HiSAN.
PRIMARY PUBLICATION: Deep Active Learning for Classifying Cancer Pathology Reports
INPUT DATA TYPE: Tokenized Text
INPUT DATA FORMAT: Unspecified
LEVEL OF DOCUMENTATION: Minimal
AVAILABLE ON GITHUB