MODEL
OVERVIEW
- Pipeline is composed of two DNN Models and a final Random Forest Model
- MRI DNN Model – processes MRI Images
- Ultra-Sound (US) DNN Model – processes ultrasound Images
- Random Forest – processes output from MRI and ultrasound model along with patient metadata
Deep Neural Network MRI Model
Model Parameters
- Framework – PyTorch
- Model: Resnet18
- EC2: g4dn.xlarge
- Single Node
- GPU: T4
- Epoch: 120
- Batch Size: 22
- Automatic Mixed Precision
- Pixel Crop: 56
- Images To Use: 25
- LR: Cosine Annealing
- Image Size: 700
- Learning Rate Start: 0.01
Features:
- MRI Images
Label:
- Biopsy Data → Binary Cancer Presence, from Cancer in Core %
- If Cancer in Core % > 0 | Label = Pos
- If Cancer in Core % = 0 | Label = Neg
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Deep Neural Network Ultrasound MODEL
Model Parameters
- Framework – PyTorch
- Model: Resnet18
- EC2: g4dn.xlarge
- Single Node
- GPU: T4
- Epoch: 120
- Batch Size: 22
- Automatic Mixed Precision
- Pixel Crop: 56
- Images To Use: 25
- LR: Cosine Annealing
- Image Size: 700
- Learning Rate Start: 0.01
Features:
- US Images
Label:
- Biopsy Data → Binary Cancer Presence, from Cancer in Core %
- If Cancer in Core % > 0 | Label = Pos
- If Cancer in Core % = 0 | Label = Neg
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ENSEMBLE MODEL
- Random Forest Model was selected as it has the best results and the results have no false negatives
- Perfect true positive rate, every patient who does have cancer is correctly classified by the model
- In a healthcare setting, especially with a condition such as cancer, the consequences of a false negative far outweigh the consequences of a false positive
Model Parameters
- Framework – scikit-learn
- Model – Random Forrest
- Estimators – 100
Features:
- DNN MRI→ Cancer Probability (0.0-1.0)
- DNN US→ Cancer Probability (0.0-1.0)
- DICOM→ Age | Height | Weight | Ethnicity
- Blood Test → PSA protein concentration
Label:
- Biopsy Data → Binary Cancer Presence, from Cancer in Core %
- If Cancer in Core % > 0 | Label = Pos
- If Cancer in Core % = 0 | Label = Neg
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