| ---
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| license: mit
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| language:
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| - en
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| library_name: pytorch
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| pipeline_tag: image-classification
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| tags:
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| - chexvision
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| - medical-imaging
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| - chest-xray
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| - radiology
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| - pytorch
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| - multi-label-classification
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| datasets:
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| - HlexNC/chest-xray-14-320
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| ---
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|
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| # CheXVision-ResNet
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|
|
| ## Project Resources
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|
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| - [GitHub repository](https://github.com/arudaev/chexvision)
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| - [Presentation deck](https://arudaev.github.io/chexvision/)
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| - [Live demo](https://huggingface.co/spaces/arudaev/chexvision-demo)
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| - [Dataset](https://huggingface.co/datasets/arudaev/chest-xray-14-320)
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|
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| > **CheXVision** — Deep Learning & Big Data university project.
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| > 14-class chest X-ray pathology detection + binary normal/abnormal classification
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| > on the NIH Chest X-ray14 dataset (112,120 images).
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|
|
| ## Architecture
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|
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| <p align="center">
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| <img src="https://huggingface.co/HlexNC/chexvision-scratch/resolve/main/arch_scratch.png" width="88%" alt="SE-ResNet Architecture"/>
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| </p>
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|
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| ## Training Pipeline
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|
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| <p align="center">
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| <img src="https://huggingface.co/HlexNC/chexvision-scratch/resolve/main/pipeline_training.png" width="42%" alt="Training Pipeline"/>
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| </p>
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|
|
| ## Training Metrics
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|
|
| - Best validation macro AUC-ROC: `0.8008`
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| - Best validation binary AUC-ROC: `0.7571`
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| - Best validation binary F1: `0.6474`
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| - Best checkpoint epoch: `60`
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|
|
|
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| ## Training Configuration
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|
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| - Repository: `HlexNC/chexvision-scratch`
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| - Dataset: [HlexNC/chest-xray-14-320](https://huggingface.co/datasets/HlexNC/chest-xray-14-320) · revision `44443e6ee968b3c6094b63f14a27698c40b50680`
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| - Architecture: Custom residual CNN with Squeeze-Excitation channel attention (depth [3, 4, 6, 3]) trained from scratch with shared features and dual classification heads.
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| - Platform: Kaggle GPU kernel (NVIDIA T4 / P100)
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| - Batch size: `24` × grad_accum `4` = **effective batch `96`**
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| - AMP (fp16): `enabled`
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| - CLAHE preprocessing: `disabled`
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| - Label smoothing: `0.0`
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| - Optimizer: AdamW · Scheduler: CosineAnnealingLR
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| - Epochs configured: `100` · Early stop patience: `15`
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|
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| ## Intended Use
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| This model is intended for research and educational work on automated chest X-ray pathology detection.
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| It outputs two predictions per image:
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| 1. **Multi-label scores** — independent sigmoid probability for each of 14 NIH pathologies
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| 2. **Binary score** — sigmoid probability of any abnormality (Normal vs. Abnormal)
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|
|
| ## Limitations
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|
|
| - Not validated for clinical use. Predictions must not substitute professional medical judgment.
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| - Trained on NIH Chest X-ray14, which contains noisy radiologist annotations (patient-level labels, not lesion-level).
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| - Performance degrades on images from equipment, patient populations, or preprocessing pipelines
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| that differ from the NIH training distribution.
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| - Reported AUC metrics are on the validation split, not the held-out test set.
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|
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| ## CheXNet Benchmark Context
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|
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| CheXNet (Rajpurkar et al., 2017) — the seminal paper establishing DenseNet-121 for chest X-ray
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| classification — reported **0.841 macro AUC-ROC** on a comparable split of this dataset.
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| CheXVision-DenseNet matches this benchmark. See the
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| [CheXVision demo](https://huggingface.co/spaces/HlexNC/chexvision-demo) for live inference.
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|
|
| ## Citation
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|
|
| ```bibtex
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| @misc{chexvision2026,
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| title={CheXVision: Dual-Task Chest X-ray Classification with Custom CNN and DenseNet-121},
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| author={BIG D(ATA) Team},
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| year={2026},
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| howpublished={\url{https://huggingface.co/HlexNC/chexvision-scratch}}
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| }
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| ```
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| |
