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Transfer_​Learning_​for_​Image_​Cancer_​Cell_​Classification

Transfer Learning for Cancer Cell Classification

When running this workflow finish executing each section before starting the next. Each portion of the workflow creates files referenced by the next.

1) Download the Dataset: this section automatically downloads the required images for you!

2) Preprocessing: this section reads all the images into a knime format and chops them up into patches that will be fed into our model later

3) Train Model: the final section reads VGG16, adds layers, trains, and scores our new model.



Loading and preprocessing the images Downloading the dataset Read VGG16 model and add flatten / add layers Read in training data and prep for VGG16 Model ImportantWhen running this workflow completely finish executing each section before starting the next. Each portion of the workflow creates files referenced bythe next. Download Histology ImagesRead VGG16Define directoryfor downloaded dataUnzip the fileCache linksAppendclass columnReading testing patchesOnly train 3 new layersFlatten output to 2048 neurons64 neuronsReLUDrop Rate = 0.53 neuronsSoftmaxArrange classes into [3] array for input intoNetworkReading training patchestestPartition ImagestrainRead imagelocationsSave model GET Request Binary Objectsto Files Keras NetworkReader Create Directory Unzip Files Table Writer List Files Rule Engine Table Reader Keras Freeze Layers Keras Flatten Layer Keras Dense Layer Keras Dropout Layer Keras Dense Layer Keras NetworkLearner One to Many Table Reader Table Writer Category To Number Load and preprocessimages (Local Files) Partitioning Table Writer Load and preprocessimages (Local Files) Table Reader Keras NetworkExecutor Table Rowto Variable Scorer Extract predictions Keras NetworkWriter Loading and preprocessing the images Downloading the dataset Read VGG16 model and add flatten / add layers Read in training data and prep for VGG16 Model ImportantWhen running this workflow completely finish executing each section before starting the next. Each portion of the workflow creates files referenced bythe next. Download Histology ImagesRead VGG16Define directoryfor downloaded dataUnzip the fileCache linksAppendclass columnReading testing patchesOnly train 3 new layersFlatten output to 2048 neurons64 neuronsReLUDrop Rate = 0.53 neuronsSoftmaxArrange classes into [3] array for input intoNetworkReading training patchestestPartition ImagestrainRead imagelocationsSave model GET Request Binary Objectsto Files Keras NetworkReader Create Directory Unzip Files Table Writer List Files Rule Engine Table Reader Keras Freeze Layers Keras Flatten Layer Keras Dense Layer Keras Dropout Layer Keras Dense Layer Keras NetworkLearner One to Many Table Reader Table Writer Category To Number Load and preprocessimages (Local Files) Partitioning Table Writer Load and preprocessimages (Local Files) Table Reader Keras NetworkExecutor Table Rowto Variable Scorer Extract predictions Keras NetworkWriter

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Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: jon
Created at: 2017-10-28
On NodePit since: 2020-12-17
Last update: 2024-04-28
Created with KNIME version: v4.2.3
Tags: Life ScienceImage AnalysisDeep LearningKeras

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