CheXVision-ResNet

CheXVision — Deep Learning & Big Data university project. 14-class chest X-ray pathology detection + binary normal/abnormal classification on the NIH Chest X-ray14 dataset (112,120 images).

Project Resources

• GitHub repository
• Presentation deck
• Live demo
• Dataset

Architecture

Training Pipeline

Training Metrics

• Best validation macro AUC-ROC: 0.8141
• Best validation binary AUC-ROC: 0.7739
• Best validation binary F1: 0.6587
• Best checkpoint epoch: 41

Per-Class AUC-ROC at Best Epoch

Training Configuration

• Repository: arudaev/chexvision-scratch
• Dataset: arudaev/chest-xray-14-320 · revision 44443e6ee968b3c6094b63f14a27698c40b50680
• Architecture: Custom residual CNN with Squeeze-Excitation channel attention (depth [3, 4, 6, 3]) trained from scratch with shared features and dual classification heads.
• Platform: Kaggle GPU kernel (NVIDIA T4 / P100)
• Batch size: 24 × grad_accum 4 = effective batch 96
• AMP (fp16): enabled
• CLAHE preprocessing: enabled
• Label smoothing: 0.1
• Optimizer: AdamW · Scheduler: CosineAnnealingLR
• Epochs configured: 100 · Early stop patience: 15

Intended Use

1. Multi-label scores — independent sigmoid probability for each of 14 NIH pathologies
2. Binary score — sigmoid probability of any abnormality (Normal vs. Abnormal)

Limitations

• Not validated for clinical use. Predictions must not substitute professional medical judgment.
• Trained on NIH Chest X-ray14, which contains noisy radiologist annotations (patient-level labels, not lesion-level).
• Performance degrades on images from equipment, patient populations, or preprocessing pipelines that differ from the NIH training distribution.
• Reported AUC metrics are on the validation split, not the held-out test set.

CheXNet Benchmark Context

Citation