File size: 3,489 Bytes
f3c2101 b4be829 f3c2101 8bf278d f3c2101 8bf278d f3c2101 | 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 | ---
license: mit
language:
- en
library_name: pytorch
pipeline_tag: image-classification
tags:
- chexvision
- medical-imaging
- chest-xray
- radiology
- pytorch
- multi-label-classification
datasets:
- HlexNC/chest-xray-14-320
---
# CheXVision-ResNet
## Project Resources
- [GitHub repository](https://github.com/arudaev/chexvision)
- [Presentation deck](https://arudaev.github.io/chexvision/)
- [Live demo](https://huggingface.co/spaces/arudaev/chexvision-demo)
- [Dataset](https://huggingface.co/datasets/arudaev/chest-xray-14-320)
> **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).
## Architecture
<p align="center">
<img src="https://huggingface.co/HlexNC/chexvision-scratch/resolve/main/arch_scratch.png" width="88%" alt="SE-ResNet Architecture"/>
</p>
## Training Pipeline
<p align="center">
<img src="https://huggingface.co/HlexNC/chexvision-scratch/resolve/main/pipeline_training.png" width="42%" alt="Training Pipeline"/>
</p>
## Training Metrics
- Best validation macro AUC-ROC: `0.8008`
- Best validation binary AUC-ROC: `0.7571`
- Best validation binary F1: `0.6474`
- Best checkpoint epoch: `60`
## Training Configuration
- Repository: `HlexNC/chexvision-scratch`
- Dataset: [HlexNC/chest-xray-14-320](https://huggingface.co/datasets/HlexNC/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: `disabled`
- Label smoothing: `0.0`
- Optimizer: AdamW · Scheduler: CosineAnnealingLR
- Epochs configured: `100` · Early stop patience: `15`
## Intended Use
This model is intended for research and educational work on automated chest X-ray pathology detection.
It outputs two predictions per image:
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
CheXNet (Rajpurkar et al., 2017) — the seminal paper establishing DenseNet-121 for chest X-ray
classification — reported **0.841 macro AUC-ROC** on a comparable split of this dataset.
CheXVision-DenseNet matches this benchmark. See the
[CheXVision demo](https://huggingface.co/spaces/HlexNC/chexvision-demo) for live inference.
## Citation
```bibtex
@misc{chexvision2026,
title={CheXVision: Dual-Task Chest X-ray Classification with Custom CNN and DenseNet-121},
author={BIG D(ATA) Team},
year={2026},
howpublished={\url{https://huggingface.co/HlexNC/chexvision-scratch}}
}
```
|