mlsanityData to 7 Oct 2026

Adaptive Contrast for Image Regression in Computer-Aided Disease Assessment

arXiv 2112.11700 ↗First posted 22 Dec 2021cs.CVcited by 16

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Abstract (arXiv)

Image regression tasks for medical applications, such as bone mineral density (BMD) estimation and left-ventricular ejection fraction (LVEF) prediction, play an important role in computer-aided disease assessment. Most deep regression methods train the neural network with a single regression loss function like MSE or L1 loss. In this paper, we propose the first contrastive learning framework for deep image regression, namely AdaCon, which consists of a feature learning branch via a novel adaptive-margin contrastive loss and a regression prediction branch. Our method incorporates label distance relationships as part of the learned feature representations, which allows for better performance in downstream regression tasks. Moreover, it can be used as a plug-and-play module to improve performance of existing regression methods. We demonstrate the effectiveness of AdaCon on two medical image regression tasks, ie, bone mineral density estimation from X-ray images and left-ventricular ejection fraction prediction from echocardiogram videos. AdaCon leads to relative improvements of 3.3% and 5.9% in MAE over state-of-the-art BMD estimation and LVEF prediction methods, respectively.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 35 of its 76 references that are in the map. First posted on arXiv 22 Dec 2021.

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35 of 76 references.

References · 35

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  3. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
  4. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  5. 2020Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentscited by 2,845
  6. 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,833
  7. 2020Supervised Contrastive Learningcited by 2,803
  8. 2020Improved Baselines with Momentum Contrastive Learningcited by 2,282
  9. 2017The Kinetics Human Action Video Datasetcited by 1,894
  10. 2021An Empirical Study of Training Self-Supervised Vision Transformerscited by 1,610
  11. 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,586
  12. 2016Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzlescited by 1,379
23 more
  1. 2020Graph Contrastive Learning with Augmentationscited by 1,203
  2. 2017A Closer Look at Spatiotemporal Convolutions for Action Recognitioncited by 1,139
  3. 2020What Makes for Good Views for Contrastive Learning?cited by 843
  4. 2020Prototypical Contrastive Learning of Unsupervised Representationscited by 636
  5. 2020Dense Contrastive Learning for Self-Supervised Visual Pre-Trainingcited by 512
  6. 2020UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentationcited by 331
  7. 2016Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentationcited by 263
  8. 2019Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiographycited by 243
  9. 2019Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentationcited by 138
  10. 2021TCLR: Temporal Contrastive Learning for Video Representationcited by 136
  11. 2021Contrastive Learning based Hybrid Networks for Long-Tailed Image Classificationcited by 130
  12. 2020Contrastive Learning for Label-Efficient Semantic Segmentationcited by 93
  13. 2019AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations rather than Datacited by 77
  14. 2019Augment your batch: better training with larger batchescited by 37
  15. 2019CANet: Cross-disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Gradingcited by 35
  16. 2021Ultrasound Video Transformers for Cardiac Ejection Fraction Estimationcited by 34
  17. 2015Robust Optimization for Deep Regressioncited by 22
  18. 2019A weakly supervised adaptive triplet loss for deep metric learningcited by 9
  19. 2021Semi-Supervised Learning for Bone Mineral Density Estimation in Hip X-ray Imagescited by 6
  20. 2019Automatic Hip Fracture Identification and Functional Subclassification with Deep Learningcited by 5
  21. 2020A New Window Loss Function for Bone Fracture Detection and Localization in X-ray Images with Point-based Annotationcited by 4
  22. 2018Multi-views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Imagescited by 3
  23. 2021Opportunistic Screening of Osteoporosis Using Plain Film Chest X-raycited by 3

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Citing papers per year: 2022: 3; 2023: 6; 2024: 4; 2025: 3.

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  1. 2024Teach CLIP to Develop a Number Sense for Ordinal Regressioncited by 20
  2. 2023Semi-Supervised Deep Regression with Uncertainty Consistency and Variational Model Ensembling via Bayesian Neural Networkscited by 15
  3. 2022Cyclical Self-Supervision for Semi-Supervised Ejection Fraction Prediction from Echocardiogram Videoscited by 12
  4. 2022TractoSCR: A Novel Supervised Contrastive Regression Framework for Prediction of Neurocognitive Measures Using Multi-Site Harmonized Diffusion MRI Tractographycited by 9
  5. 2024CoReEcho: Continuous Representation Learning for 2D+time Echocardiography Analysiscited by 8
  6. 2024Urban Safety Perception Assessments via Integrating Multimodal Large Language Models with Street View Imagescited by 7
  7. 2023SupReMix: Supervised Contrastive Learning for Medical Imaging Regression with Mixupcited by 3
  8. 2023Joint Self-Supervised and Supervised Contrastive Learning for Multimodal MRI Data: Towards Predicting Abnormal Neurodevelopmentcited by 3
  9. 2023Learning Generalized Hybrid Proximity Representation for Image Recognitioncited by 2
  10. 2024In-Pixel Foreground and Contrast Enhancement Circuits with Customizable Mappingcited by 2
  11. 2022Regression Metric Loss: Learning a Semantic Representation Space for Medical Imagescited by 1
  12. 2023SCOL: Supervised Contrastive Ordinal Loss for Abdominal Aortic Calcification Scoring on Vertebral Fracture Assessment Scanscited by 1
4 more
  1. 2023M(otion)-mode Based Prediction of Ejection Fraction using Echocardiogramscited by 1
  2. 2025ACCon: Angle-Compensated Contrastive Regularizer for Deep Regressioncited by 1
  3. 2025CardiacCLIP: Video-based CLIP Adaptation for LVEF Prediction in a Few-shot Mannercited by 0
  4. 2025Contrastive Learning for Semi-Supervised Deep Regression with Generalized Ordinal Rankings from Spectral Seriationcited by 0