rpotter6298
8a136c71fe
- Created `classification.py` for comparing image-level and patient-level classification results using various CNN models. - Implemented `create_patient_groups.py` to extract features, generate PCA/t-SNE plots, and identify patient groups via K-means clustering. - Added `figure6.py` to generate boxplots for test accuracy across multiple seeds. - Developed `simple_patient_tsne.py` to perform t-SNE visualization of patient groups and save results in a manifest file. - Introduced `simple_patient_manifest.csv` to store patient IDs, classes, image counts, and associated images.
52 lines
1.0 KiB
JSON
52 lines
1.0 KiB
JSON
[
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{
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"model": "VGG16",
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"image_cv": 0.992,
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"image_test": 1.0,
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"image_nfeat": 5000,
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"patient_cv": 0.9122,
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"patient_test": 0.8933,
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"patient_nfeat": 200,
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"drop": 0.1067
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},
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{
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"model": "DenseNet121",
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"image_cv": 0.9875,
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"image_test": 0.9955,
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"image_nfeat": 5000,
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"patient_cv": 0.9367,
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"patient_test": 0.8711,
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"patient_nfeat": 1000,
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"drop": 0.1243
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},
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{
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"model": "EfficientNetB1",
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"image_cv": 0.9829,
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"image_test": 0.9955,
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"image_nfeat": 5000,
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"patient_cv": 0.9158,
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"patient_test": 0.8844,
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"patient_nfeat": 1000,
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"drop": 0.111
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},
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{
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"model": "MobileNetV2",
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"image_cv": 0.9875,
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"image_test": 0.9955,
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"image_nfeat": 2000,
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"patient_cv": 0.913,
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"patient_test": 0.8889,
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"patient_nfeat": 750,
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"drop": 0.1066
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},
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{
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"model": "ResNet50",
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"image_cv": 0.967,
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"image_test": 0.9773,
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"image_nfeat": 750,
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"patient_cv": 0.8742,
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"patient_test": 0.8622,
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"patient_nfeat": 400,
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"drop": 0.1151
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}
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] |