Stabilized Medical Image Attacks
Authors: Gege Qi, Lijun GONG, Yibing Song, Kai Ma, Yefeng Zheng
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on several medical image analysis benchmarks including the recent COVID-19 dataset show the stability of the proposed method. |
| Researcher Affiliation | Industry | Gege Qi1, Lijun Gong1 , Yibing Song2 , Kai Ma1, Yefeng Zheng1 1 Tencent Jarvis Lab, Shenzhen, China 2 Tencent AI Lab, Shenzhen, China |
| Pseudocode | Yes | We provide more results including using different Gaussian kernels W and show the pseudo code in the supplementary files. |
| Open Source Code | Yes | The code is available at https://github.com/ imogenqi/SMA |
| Open Datasets | Yes | We use two datasets for diabetic retinopathy grading. One is the APTOS-2019 (APT, 2019) dataset with 3,662 fundus images. The other is a large-scale Kaggle-DR (Kag, 2015) dataset where we randomly select 11,000 fundus images from its original training set. Both APTOS-2019 and Kaggle DR contains five defined categories. For artefact detection we use EAD-2019 (EAD, 2019) dataset with 2,500 images collected from endoscopic video frames... For lung segmentation, we use the COVID-19 dataset (COV, 2019). |
| Dataset Splits | No | The paper mentions using a 'training set' for Kaggle-DR but does not specify exact train/validation/test splits (e.g., percentages or sample counts) or any cross-validation setup for any of the datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions deep learning architectures like 'Res Net-50', 'graph convolutional network', 'multi-scale booster framework', and 'U-Net', but it does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | During perturbation generation, we set ϵ as 0.05, 0.01, 5 × 10−5 for lung segmentation, artefact detection and diabetic retinopathy grading respectively. We stop at the 10-th iteration for all the attack methods. The α in Eq. 4 is set as 1. |