mlsanityData to 7 Oct 2026

Learning to diagnose from scratch by exploiting dependencies among labels

arXiv 1710.10501 ↗First posted 28 Oct 2017cs.CVcited by 33

Not on the map. Why?

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.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Oct 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 4; 2023: 3; 2024: 3; 2025: 7; 2026: 5.

33 papers.

Sort:

By area:

  1. 2021Label-Assemble: Leveraging Multiple Datasets with Partial Labelscited by 20
  2. 2021Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attackcited by 15
  3. 2021RadBERT-CL: Factually-Aware Contrastive Learning For Radiology Report Classificationcited by 13
  4. 2021SCALP -- Supervised Contrastive Learning for Cardiopulmonary Disease Classification and Localization in Chest X-rays using Patient Metadatacited by 9
  5. 2022Radiomics-Guided Global-Local Transformer for Weakly Supervised Pathology Localization in Chest X-Rayscited by 7
  6. 2021Knowledge-Augmented Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loopcited by 6
  7. 2021Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessmentcited by 6
  8. 2023HydraViT: Adaptive Multi-Branch Transformer for Multi-Label Disease Classification from Chest X-ray Imagescited by 6
  9. 2021Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformationscited by 5
  10. 2021Multi-Label Generalized Zero Shot Learning for the Classification of Disease in Chest Radiographscited by 5
  11. 2022ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classificationcited by 5
  12. 2023SynthEnsemble: A Fusion of CNN, Vision Transformer, and Hybrid Models for Multi-Label Chest X-Ray Classificationcited by 5
21 more
  1. 2024MedMAE: A Self-Supervised Backbone for Medical Imaging Taskscited by 5
  2. 2022RoS-KD: A Robust Stochastic Knowledge Distillation Approach for Noisy Medical Imagingcited by 4
  3. 2023BB-GCN: A Bi-modal Bridged Graph Convolutional Network for Multi-label Chest X-Ray Recognitioncited by 4
  4. 2021A Relational-learning Perspective to Multi-label Chest X-ray Classificationcited by 3
  5. 2024Computer-Aided Diagnosis of Thoracic Diseases in Chest X-rays using hybrid CNN-Transformer Architecturecited by 3
  6. 2021Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identificationcited by 2
  7. 2025DeepChest: Dynamic Gradient-Free Task Weighting for Effective Multi-Task Learning in Chest X-ray Classificationcited by 2
  8. 2025In-Model Merging for Enhancing the Robustness of Medical Imaging Classification Modelscited by 1
  9. 2025CLARiTy: A Vision Transformer for Multi-Label Classification and Weakly-Supervised Localization of Chest X-ray Pathologiescited by 1
  10. 2021Weighing Features of Lung and Heart Regions for Thoracic Disease Classificationcited by 0
  11. 2022Early Diagnosis of Chronic Obstructive Pulmonary Disease from Chest X-Rays using Transfer Learning and Fusion Strategiescited by 0
  12. 2024More Than Positive and Negative: Communicating Fine Granularity in Medical Diagnosiscited by 0
  13. 2025Diagnosis of Patients with Viral, Bacterial, and Non-Pneumonia Based on Chest X-Ray Images Using Convolutional Neural Networkscited by 0
  14. 2025MetaChest: Generalized few-shot learning of pathologies from chest X-rayscited by 0
  15. 2025MedDChest: A Content-Aware Multimodal Foundational Vision Model for Thoracic Imagingcited by 0
  16. 2025Learning Sparse Label Couplings for Multilabel Chest X-Ray Diagnosiscited by 0
  17. 2026CBAM-Enhanced DenseNet121 for Multi-Class Chest X-Ray Classification with Grad-CAM Explainabilitycited by 0
  18. 2026Improving Imbalanced Multi-Label Chest X-Ray Diagnosis via CBAM-Enhanced CNN Backbonescited by 0
  19. 2026Leveraging Pathology Co-occurrence for Test-Time Adaptation in Chest X-Ray Diagnosiscited by 0
  20. 2026PulmoSight-XAI: An Explainable Multi-View Attention Ensemble with Gradient Boosting Meta-Learning for Multi-Label Chest X-Ray Classificationcited by 0
  21. 2026AlphaRAD: Grounded Zero-Shot Classification in Chest Radiology via α-Corrected Binary Cross Entropy and Factorized Latent Supervisioncited by 0