CXR Lines and Tubes

Model description

1. an EfficientNet-B1 encoder with separate U-Net decoders for device-agnostic and device-family line/distal-tip masks;
2. an EfficientNetV2-B0/DeepLabV3+ carina heatmap and coordinate model;
3. a five-fold EfficientNet-B1 multilabel classifier using RGB plus predicted device and carina probability maps; and
4. a lightweight FastViT four-family presence classifier for ETT, NGT, CVC, and Swan-Ganz screening.

Intended use

Inputs and outputs

Device model

• Input: one grayscale chest radiograph, directly resized to 1024 x 1024.
• Agnostic outputs: line/tube union and distal-tip union probabilities.
• Family outputs: independent line and distal-tip probabilities for ETT, NGT, CVC, and Swan Ganz.
• Each device polyline contributes one distal-tip target: the most inferior endpoint for ETT/NGT and the endpoint closest to normalized image center for CVC/Swan-Ganz. An incompletely imaged NGT uses its most inferior visible endpoint, which is not necessarily its true anatomical tip.

Carina model

• Input: one grayscale chest radiograph, directly resized to 512 x 512.
• Outputs: carina heatmap, differentiable normalized coordinate, and peak coordinate.

Classifier

• Input: a 512 x 512 six-channel tensor consisting of RGB plus agnostic line, distal-tip, and carina probabilities.
• Output labels:
  • ETT Abnormal
  • ETT Borderline
  • ETT Normal
  • NGT Abnormal
  • NGT Borderline
  • NGT Incompletely Imaged
  • NGT Normal
  • CVC Abnormal
  • CVC Borderline
  • CVC Normal
  • Swan Ganz Catheter Present

Lightweight device-presence classifier

• Input: one grayscale chest radiograph, directly resized to 384 x 384.
• Outputs: independent ETT, NGT, CVC, and Swan-Ganz presence probabilities.
• Training includes an inexpensive three-block depthwise-separable U-Net decoder for auxiliary family centerline supervision.
• Only the encoder, pooling layer, and four-logit classification head are needed for production screening.

Training data

Training procedure

• Five patient-disjoint folds with approximately balanced competition labels.
• Direct square resize without aspect-ratio preservation or padding.
• ImageNet initialization for all encoders/classifier backbones.
• Device Stage 1: distal-v4 B1 regular U-Net, 1024, 30 epochs, BCE plus low-weight line and distal-tip soft Dice.
• Device Stage 2: frozen eval-mode Stage 1 encoder, separate family U-Net decoder, 1024, 15 epochs; Swan-Ganz is sampled to approximately 10% prevalence.
• Carina: B0 DeepLabV3+, 512, 30 epochs, BCE plus coordinate Smooth L1.
• Classifier: B1, 512, 30 epochs, BF16 mixed precision.
• Segmentation features for annotated classifier images are strictly OOF; task-unannotated images use a five-fold probability ensemble.

Evaluation

Kaggle late-submission evaluation

• Add a concatenated six-channel OOF AUROC if it is generated.
• Add per-label AUROC.
• Include confidence intervals or fold standard deviation where appropriate.

Limitations and biases

• Single competition dataset; external-site generalization is unknown.
• Synthetic thick polyline masks are not precise device-boundary annotations.
• The incompletely imaged NGT target is a visible inferior endpoint rather than a guaranteed anatomical tip.
• Rare Swan-Ganz examples limit family segmentation reliability.
• The presence classifier's NIH labels are model-derived and can contain label errors despite the manually selected CVC cutoff.
• The remaining NIH cohort has no hard device-presence labels and was selected by an earlier model. Its prediction distribution cannot establish prevalence or calibration.
• Devices can overlap, exit the field of view, or be incompletely imaged.
• Performance may change with acquisition hardware, post-processing, image orientation, patient population, anatomy, disease burden, and unseen devices.
• AUROC does not establish calibration or a safe clinical operating point.
• OOF/ensemble feature routing matches this research protocol and must be reproduced exactly for comparable results.

Ethical and safety considerations

Hugging Face Space integration

• display the original radiograph alongside continuous-probability overlays;
• report device-presence probabilities and the exact model version;
• display the active profile and fold IDs for every model component;
• allow independent fold-count overrides and a force-full-pipeline option;
• keep screening thresholds configurable until externally validated;
• explain when the larger pipeline was skipped; and
• present a prominent research-only, non-clinical-use warning.

Release contents

• Five device-agnostic checkpoints.
• Five device-family decoder checkpoints.
• Five carina checkpoints.
• Five final classifier checkpoints.
• Final device-family presence classifier checkpoints.
• Versioned config module for every model group.
• Exact class/channel ordering.
• Preprocessing and normalization code.
• Ensemble profiles and inference runtime.
• Checkpoint SHA-256 hashes.
• Dependency requirements.
• Final Kaggle K1-K5 public/private metrics.
• Final CV metrics for the device-family presence classifier.
• Dataset, code, and model-weight licensing statement.
• Dataset citations and source repository.
• Space inference wrapper and UI.
• Versioned fold-selection manifest and screen/fast/balanced/max runtime profiles.

Reproducibility

Dataset citations

• Royal Australian and New Zealand College of Radiologists. RANZCR CLiP - Catheter and Line Position Challenge. Kaggle.
• Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. ChestX-ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. CVPR 2017, 2097–2106.
• NIH ChestX-ray14 download: https://nihcc.app.box.com/v/ChestXray-NIHCC.

Space input and ETT–carina measurement