.png){kind=icon}
返回
Explainable Knowledge Distillation for On-Device Chest X-Ray Classification
.png){kind=icon}
OA
AI
DOI：10.1109/TCBB.2023.3272333.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Automated multi-label chest X-rays (CXR) image classification has achieved substantial progress in clinical diagnosis via utilizing sophisticated deep learning approaches. However, most deep models have high computational demands, which makes them less feasible for compact devices with low computational requirements. To overcome this problem, we propose a knowledge distillation (KD) strategy to create the compact deep learning model for the real-time multi-label CXR image classification. We study different alternatives of CNNs and Transforms as the teacher to distill the knowledge to a smaller student. Then, we employed explainable artificial intelligence (XAI) to provide the visual explanation for the model decision improved by the KD. Our results on three benchmark CXR datasets show that our KD strategy provides the improved performance on the compact student model, thus being the feasible choice for many limited hardware platforms. For instance, when using DenseNet161 as the teacher network, EEEA-Net-C2 achieved an AUC of 83.7%, 87.1%, and 88.7% on the ChestX-ray14, CheXpert, and PadChest datasets, respectively, with fewer parameters of 4.7 million and computational cost of 0.3 billion FLOPS.
Keyword:
Computational modeling
Transformers
Diseases
Deep learning
Computer architecture
Artificial intelligence
Image classification
Knowledge distillation
chest X-ray
explainable artificial intelligence
on-device
期刊
I
IF:
3.4
论文数:
3.3K
被引数:
6.4K
机构
引用论文
.png){kind=icon}
Enhanced Diagnosis of Pneumothorax with an Improved Real-Time Augmentation for Imbalanced Chest X-rays Data Based on DCNN基于DCNN的不平衡胸部x线数据的改进实时增强对气胸的诊断
PREAI
分享收藏A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI可解释人工智能 (XAI) 调查: 走向医学XAI
OAAI
OAAI分享收藏分享收藏
查看更多内容