# Learning to diagnose from scratch by exploiting dependencies among labels

arXiv: https://arxiv.org/abs/1710.10501 · HTML: https://mlsanity.com/paper/1710.10501/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2017.

## Built on by (most cited first, 30 of 33)

- [Label-Assemble: Leveraging Multiple Datasets with Partial Labels](https://mlsanity.com/paper/2109.12265/) (2021)
- [Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack](https://mlsanity.com/paper/2103.03438/) (2021)
- [RadBERT-CL: Factually-Aware Contrastive Learning For Radiology Report Classification](https://mlsanity.com/paper/2110.15426/) (2021)
- [SCALP -- Supervised Contrastive Learning for Cardiopulmonary Disease Classification and Localization in Chest X-rays using Patient Metadata](https://mlsanity.com/paper/2110.14787/) (2021)
- [Radiomics-Guided Global-Local Transformer for Weakly Supervised Pathology Localization in Chest X-Rays](https://mlsanity.com/paper/2207.04394/) (2022)
- [Knowledge-Augmented Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop](https://mlsanity.com/paper/2104.04968/) (2021)
- [Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment](https://mlsanity.com/paper/2104.05261/) (2021)
- [HydraViT: Adaptive Multi-Branch Transformer for Multi-Label Disease Classification from Chest X-ray Images](https://mlsanity.com/paper/2310.06143/) (2023)
- [Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations](https://mlsanity.com/paper/2101.07945/) (2021)
- [Multi-Label Generalized Zero Shot Learning for the Classification of Disease in Chest Radiographs](https://mlsanity.com/paper/2107.06563/) (2021)
- [ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classification](https://mlsanity.com/paper/2210.02998/) (2022)
- [SynthEnsemble: A Fusion of CNN, Vision Transformer, and Hybrid Models for Multi-Label Chest X-Ray Classification](https://mlsanity.com/paper/2311.07750/) (2023)
- [MedMAE: A Self-Supervised Backbone for Medical Imaging Tasks](https://mlsanity.com/paper/2407.14784/) (2024)
- [RoS-KD: A Robust Stochastic Knowledge Distillation Approach for Noisy Medical Imaging](https://mlsanity.com/paper/2210.08388/) (2022)
- [BB-GCN: A Bi-modal Bridged Graph Convolutional Network for Multi-label Chest X-Ray Recognition](https://mlsanity.com/paper/2302.11082/) (2023)
- [A Relational-learning Perspective to Multi-label Chest X-ray Classification](https://mlsanity.com/paper/2103.06220/) (2021)
- [Computer-Aided Diagnosis of Thoracic Diseases in Chest X-rays using hybrid CNN-Transformer Architecture](https://mlsanity.com/paper/2404.11843/) (2024)
- [Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification](https://mlsanity.com/paper/2102.13269/) (2021)
- [DeepChest: Dynamic Gradient-Free Task Weighting for Effective Multi-Task Learning in Chest X-ray Classification](https://mlsanity.com/paper/2505.23595/) (2025)
- [In-Model Merging for Enhancing the Robustness of Medical Imaging Classification Models](https://mlsanity.com/paper/2502.20516/) (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)
- [Early Diagnosis of Chronic Obstructive Pulmonary Disease from Chest X-Rays using Transfer Learning and Fusion Strategies](https://mlsanity.com/paper/2211.06925/) (2022)
- [More Than Positive and Negative: Communicating Fine Granularity in Medical Diagnosis](https://mlsanity.com/paper/2408.02214/) (2024)
- [Diagnosis of Patients with Viral, Bacterial, and Non-Pneumonia Based on Chest X-Ray Images Using Convolutional Neural Networks](https://mlsanity.com/paper/2503.02906/) (2025)
- [MetaChest: Generalized few-shot learning of pathologies from chest X-rays](https://mlsanity.com/paper/2509.25590/) (2025)
- [MedDChest: A Content-Aware Multimodal Foundational Vision Model for Thoracic Imaging](https://mlsanity.com/paper/2511.04016/) (2025)
- [Learning Sparse Label Couplings for Multilabel Chest X-Ray Diagnosis](https://mlsanity.com/paper/2511.07801/) (2025)
- [CBAM-Enhanced DenseNet121 for Multi-Class Chest X-Ray Classification with Grad-CAM Explainability](https://mlsanity.com/paper/2604.12305/) (2026)
- [Improving Imbalanced Multi-Label Chest X-Ray Diagnosis via CBAM-Enhanced CNN Backbones](https://mlsanity.com/paper/2605.02328/) (2026)

## Abstract (arXiv)

The field of medical diagnostics contains a wealth of challenges which closely resemble classical machine learning problems; practical constraints, however, complicate the translation of these endpoints naively into classical architectures. Many tasks in radiology, for example, are largely problems of multi-label classification wherein medical images are interpreted to indicate multiple present or suspected pathologies. Clinical settings drive the necessity for high accuracy simultaneously across a multitude of pathological outcomes and greatly limit the utility of tools which consider only a subset. This issue is exacerbated by a general scarcity of training data and maximizes the need to extract clinically relevant features from available samples -- ideally without the use of pre-trained models which may carry forward undesirable biases from tangentially related tasks. We present and evaluate a partial solution to these constraints in using LSTMs to leverage interdependencies among target labels in predicting 14 pathologic patterns from chest x-rays and establish state of the art results on the largest publicly available chest x-ray dataset from the NIH without pre-training. Furthermore, we propose and discuss alternative evaluation metrics and their relevance in clinical practice.