Datasets:
File size: 3,274 Bytes
fe39cc9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | # Multi-Task Biomarker Configuration — Published Experiments
#
# Pass this file to --biomarker_config in train.py or test.py.
#
# For a minimal single-task example with detailed field descriptions,
# see biomarker_config_single_task_example.yaml.
experiment_name: "comorbidities_detection_multitask"
description: "Multi-task prediction of comorbidities and demographics from AbdCTBench"
# -------------------------------------------------------------------------
# Binary classification tasks (9 targets)
# -------------------------------------------------------------------------
binary_biomarkers:
- name: "MORTALITY"
description: "Death in the followup period"
positive_class: "PRESENT"
- name: "HCC12"
description: "HCC code 12 — Breast, Prostate, and other cancers"
positive_class: "PRESENT"
- name: "HCC18"
description: "HCC code 18 — Diabetes with Chronic Complications"
positive_class: "PRESENT"
- name: "HCC96"
description: "HCC code 96 — Cardiac arrhythmias"
positive_class: "PRESENT"
- name: "HCC108"
description: "HCC code 108 — Vascular disease"
positive_class: "PRESENT"
- name: "HCC111"
description: "HCC code 111 — Chronic obstructive pulmonary disease"
positive_class: "PRESENT"
- name: "T2D"
description: "Type 2 Diabetes"
positive_class: "PRESENT"
- name: "MI"
description: "Myocardial Infarction"
positive_class: "PRESENT"
- name: "CALCIUMSCORING_ABDOMINALAGATSTON_BINARY"
description: "High abdominal aortic calcium score (Agatston > 1000)"
positive_class: "PRESENT"
# -------------------------------------------------------------------------
# Multiclass classification tasks
# -------------------------------------------------------------------------
multiclass_biomarkers: []
# -------------------------------------------------------------------------
# Continuous regression tasks (1 target)
# -------------------------------------------------------------------------
continuous_biomarkers:
- name: "AGE"
description: "Patient age in years (min-max normalized to [0, 1] during training)"
min_value: 18
max_value: 89
normalization: "min_max"
# -------------------------------------------------------------------------
# Preprocessing settings
# -------------------------------------------------------------------------
preprocessing:
image_size: 256
normalize_images: true
convert_to_rgb: true
# -------------------------------------------------------------------------
# Training settings
# -------------------------------------------------------------------------
training:
class_weighting: true
balanced_sampling: true
# -------------------------------------------------------------------------
# Validation / threshold settings
# -------------------------------------------------------------------------
validation:
threshold_optimization: true
optimization_metric: "f1_score"
per_biomarker_thresholds: true
threshold_search_range: [0.1, 0.9]
threshold_search_steps: 9
fallback_threshold: 0.5
metrics:
- "auroc"
- "accuracy"
- "sensitivity"
- "specificity"
- "f1_score"
- "precision"
- "recall"
regression_metrics:
- "mse"
- "mae"
- "r2_score"
|