AFiRe: Anatomy-Driven Self-Supervised Learning for Fine-Grained Representation in Radiographic Images
Authors: Yihang Liu, Lianghua He, Ying Wen, Longzhen Yang, Hongzhou Chen
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that AFi Re: (i) provides robust anatomical discrimination, achieving more cohesive feature clusters compared to state-of-the-art contrastive learning methods; (ii) demonstrates superior generalization, surpassing 7 radiography-specific self-supervised methods in multi-label classification tasks with limited labeling; and (iii) integrates fine-grained information, enabling precise anomaly detection using only image-level annotations. Our extensive experiments demonstrate that AFi Re enhances the model s capacity to capture fine-grained discriminative information from normal radiographic images, facilitating a more robust representation across different anatomic structures. Compared to different supervised and self-supervised benchmarks, AFi Re achieves superior performance in multi-label classification tasks, indicating its generalization in analyzing real disease images. |
| Researcher Affiliation | Academia | 1 School of Computer Science and Technology, Tongji University, Shanghai, China. 2 Shanghai Eye Diseases Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, China. 3 School of Communication and Electronic Engineering, East China Normal University, Shanghai, China. EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology using textual explanations and figures, but no explicitly labeled pseudocode or algorithm blocks are present. |
| Open Source Code | Yes | Code https://github.com/LYH-hh/AFi Re |
| Open Datasets | Yes | In this paper, we assemble a dataset of 811,170 Chest X-ray (CXR) images for pre-training, which includes 81,117 normal CXR images sourced from MIMIC-CXR-JPG (Johnson et al. 2019) and 81, 117 9 synthetic abnormal images. We evaluate our model on three CXR datasets: (i) Child CXRs (Kermany et al. 2018), (ii) NIH (Wang et al. 2017), (iii) CXP (Irvin et al. 2019), and (iv) SIIM-ARC 1, using the official data partitions. |
| Dataset Splits | Yes | We evaluate our model on three CXR datasets: (i) Child CXRs (Kermany et al. 2018), (ii) NIH (Wang et al. 2017), (iii) CXP (Irvin et al. 2019), and (iv) SIIM-ARC 1, using the official data partitions. To ensure robustness, we further conduct five-fold cross-validation for these experiments. AUC (%) for multi-label classification on NIH and CXP, and Dice (%) for segmentation on SIIM-ACR are reported. The best results are bolded. 5-C denotes the five-fold cross-validation. |
| Hardware Specification | Yes | We implement our pre-training model using Py Torch and distribute the training across 8 NVIDIA A6000 GPUs, running for 800 epochs with a batch size of 64 for 6 days. |
| Software Dependencies | No | The paper states 'We implement our pre-training model using Py Torch' but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We follow the DINO (Caron et al. 2021) training paradigm (e.g., optimizer, learning rate, and weight decay schedule), utilizing an input image size of 224 224. ...running for 800 epochs with a batch size of 64 for 6 days. ... τ is the temperature parameter and H(q, c) = PK k=1 q(k) log c(k), which is the cross-entropy function to measure the similarity between two probabilities. ...through the momentum parameter m. |