# Attention-Guided Curriculum Learning for Weakly Supervised Classification and Localization of Thoracic Diseases on Chest Radiographs

arXiv: https://arxiv.org/abs/1807.07532 · HTML: https://mlsanity.com/paper/1807.07532/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2018.

## Built on by (most cited first, 30 of 32)

- [Curriculum Learning: A Survey](https://mlsanity.com/paper/2101.10382/) (2021)
- [Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification](https://mlsanity.com/paper/2210.12843/) (2022)
- [Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws](https://mlsanity.com/paper/2410.11820/) (2024)
- [SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2111.13495/) (2021)
- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [Memory-aware curriculum federated learning for breast cancer classification](https://mlsanity.com/paper/2107.02504/) (2021)
- [Label-Assemble: Leveraging Multiple Datasets with Partial Labels](https://mlsanity.com/paper/2109.12265/) (2021)
- [Coaching a Teachable Student](https://mlsanity.com/paper/2306.10014/) (2023)
- [Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum Learning](https://mlsanity.com/paper/2204.11433/) (2022)
- [Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Images](https://mlsanity.com/paper/2403.08689/) (2024)
- [Anatomy-XNet: An Anatomy Aware Convolutional Neural Network for Thoracic Disease Classification in Chest X-rays](https://mlsanity.com/paper/2106.05915/) (2021)
- [On the Statistical Benefits of Curriculum Learning](https://mlsanity.com/paper/2111.07126/) (2021)
- [Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis](https://mlsanity.com/paper/2106.07049/) (2021)
- [Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment](https://mlsanity.com/paper/2104.05261/) (2021)
- [ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classification](https://mlsanity.com/paper/2210.02998/) (2022)
- [Weakly Supervised Object Detection in Chest X-Rays with Differentiable ROI Proposal Networks and Soft ROI Pooling](https://mlsanity.com/paper/2402.11985/) (2024)
- [CLIP: Train Faster with Less Data](https://mlsanity.com/paper/2212.01452/) (2022)
- [BB-GCN: A Bi-modal Bridged Graph Convolutional Network for Multi-label Chest X-Ray Recognition](https://mlsanity.com/paper/2302.11082/) (2023)
- [Learning Low-Rank Feature for Thorax Disease Classification](https://mlsanity.com/paper/2404.18933/) (2024)
- [GREN: Graph-Regularized Embedding Network for Weakly-Supervised Disease Localization in X-ray Images](https://mlsanity.com/paper/2107.06442/) (2021)
- [Medical Knowledge-Guided Deep Curriculum Learning for Elbow Fracture Diagnosis from X-Ray Images](https://mlsanity.com/paper/2110.10381/) (2021)
- [Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift](https://mlsanity.com/paper/2210.01891/) (2022)
- [A survey on bias in machine learning research](https://mlsanity.com/paper/2308.11254/) (2023)
- [CASPIANET++: A Multidimensional Channel-Spatial Asymmetric Attention Network with Noisy Student Curriculum Learning Paradigm for Brain Tumor Segmentation](https://mlsanity.com/paper/2107.04099/) (2021)
- [Curriculum for Crowd Counting -- Is it Worthy?](https://mlsanity.com/paper/2401.07586/) (2024)
- [In-Model Merging for Enhancing the Robustness of Medical Imaging Classification Models](https://mlsanity.com/paper/2502.20516/) (2025)
- [CLPSTNet: A Progressive Multi-Scale Convolutional Steganography Model Integrating Curriculum Learning](https://mlsanity.com/paper/2504.16364/) (2025)
- [CLARiTy: A Vision Transformer for Multi-Label Classification and Weakly-Supervised Localization of Chest X-ray Pathologies](https://mlsanity.com/paper/2512.16700/) (2025)
- [Weighing Features of Lung and Heart Regions for Thoracic Disease Classification](https://mlsanity.com/paper/2105.12430/) (2021)
- [More Than Positive and Negative: Communicating Fine Granularity in Medical Diagnosis](https://mlsanity.com/paper/2408.02214/) (2024)

## Abstract (arXiv)

In this work, we exploit the task of joint classification and weakly supervised localization of thoracic diseases from chest radiographs, with only image-level disease labels coupled with disease severity-level (DSL) information of a subset. A convolutional neural network (CNN) based attention-guided curriculum learning (AGCL) framework is presented, which leverages the severity-level attributes mined from radiology reports. Images in order of difficulty (grouped by different severity-levels) are fed to CNN to boost the learning gradually. In addition, highly confident samples (measured by classification probabilities) and their corresponding class-conditional heatmaps (generated by the CNN) are extracted and further fed into the AGCL framework to guide the learning of more distinctive convolutional features in the next iteration. A two-path network architecture is designed to regress the heatmaps from selected seed samples in addition to the original classification task. The joint learning scheme can improve the classification and localization performance along with more seed samples for the next iteration. We demonstrate the effectiveness of this iterative refinement framework via extensive experimental evaluations on the publicly available ChestXray14 dataset. AGCL achieves over 5.7% (averaged over 14 diseases) increase in classification AUC and 7%/11% increases in Recall/Precision for the localization task compared to the state of the art.