# Image-Domain Poisson-Perturbation Robustness of NCCT Slice Classification

arXiv: https://arxiv.org/abs/2603.26764 · HTML: https://mlsanity.com/paper/2603.26764/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 24 Mar 2026.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Symmetry-Enhanced Attention Network for Acute Ischemic Infarct Segmentation with Non-Contrast CT Images](https://mlsanity.com/paper/2110.05039/) (2021)
- [Task-Oriented Low-Dose CT Image Denoising](https://mlsanity.com/paper/2103.13557/) (2021)

## Abstract (arXiv)

Image-domain Poisson perturbation may alter normalized NCCT appearance and downstream models. We evaluated classification of ischemic core/penumbra-bearing non-contrast CT (NCCT) slices under five simulated settings. The source cohort was CPAISD (112 hyperacute ischemic stroke patients); the test partition contained 10 patients and 809 slices. First, a fixed-checkpoint audit compared direct ResNet-18 classification (P1) with residual U-Net denoising followed by the classifier (P2). P1 average precision (AP) ranged from 0.694 to 0.901, whereas P2 AP ranged from 0.509 to 0.797 (substantially lower at settings 10-40). The fixed 0.5 threshold had 0-12.8% sensitivity for P1 and 0% for P2. Second, a prospectively locked de novo experiment compared direct noisy classification (DNC), joint denoising-classification (JDC-0), and the same joint model with privileged training-only lesion-boundary supervision (JDC-B). Across-setting mean AP was 0.861 +/- 0.028 for DNC, 0.840 +/- 0.038 for JDC-0, and 0.856 +/- 0.031 for JDC-B. Hierarchical paired-bootstrap differences were -0.021 (95% CI -0.063 to 0.019) for JDC-0 minus DNC, 0.016 (-0.019 to 0.054) for JDC-B minus JDC-0, and -0.005 (-0.041 to 0.026) for JDC-B minus DNC; none excluded zero. A frozen stress test on the 52-patient AISD partition also showed limited transportability. Thus, ordinary joint training did not demonstrate a classification benefit, and the boundary term recovered part of its point-estimate loss without a statistically supported advantage. Image fidelity, ranking, calibration, and clinical utility must be evaluated separately. This study does not validate acquired low-dose, portable, or cone-beam CT, nor patient-level stroke diagnosis.