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Deep Learning for Medical Image Analysis

arXiv 1708.08987 ↗First posted 17 Aug 2017cs.CVcited by 399

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Abstract (arXiv)

This report describes my research activities in the Hasso Plattner Institute and summarizes my Ph.D. plan and several novels, end-to-end trainable approaches for analyzing medical images using deep learning algorithm. In this report, as an example, we explore different novel methods based on deep learning for brain abnormality detection, recognition, and segmentation. This report prepared for the doctoral consortium in the AIME-2017 conference.

Key facts. Cited by 399 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Aug 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 75; 2022: 60; 2023: 80; 2024: 66; 2025: 78; 2026: 40.

399 papers.

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  1. 2021Domain Adaptation for Medical Image Analysis: A Surveycited by 351
  2. 2022Brain Imaging Generation with Latent Diffusion Modelscited by 267
  3. 2022Transformers in Medical Imaging: A Surveycited by 245
  4. 2023Segment Anything in Medical Imagescited by 226
  5. 2024U-KAN Makes Strong Backbone for Medical Image Segmentation and Generationcited by 176
  6. 2021Causality-inspired Single-source Domain Generalization for Medical Image Segmentationcited by 118
  7. 2023ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Modelscited by 116
  8. 2022SimpleClick: Interactive Image Segmentation with Simple Vision Transformerscited by 109
  9. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  10. 2021Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyondcited by 75
  11. 2021Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Futurecited by 67
  12. 2021Recurrent Mask Refinement for Few-Shot Medical Image Segmentationcited by 63
38 more
  1. 2021Learning to Synthesize Programs as Interpretable and Generalizable Policiescited by 59
  2. 2022BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active Annotationcited by 54
  3. 2021MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentationcited by 53
  4. 2023MindfulDiary: Harnessing Large Language Model to Support Psychiatric Patients' Journalingcited by 50
  5. 2023Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRIcited by 45
  6. 2024nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Modelcited by 45
  7. 2022PseudoClick: Interactive Image Segmentation with Click Imitationcited by 42
  8. 2021A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Conceptscited by 33
  9. 2023MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Visioncited by 31
  10. 2024Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defensescited by 30
  11. 2024AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Modelscited by 29
  12. 2021iSegFormer: Interactive Segmentation via Transformers with Application to 3D Knee MR Imagescited by 27
  13. 2024From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimationcited by 27
  14. 2021More than Encoder: Introducing Transformer Decoder to Upsamplecited by 26
  15. 2021Superpixel-guided Iterative Learning from Noisy Labels for Medical Image Segmentationcited by 25
  16. 2023Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Modelcited by 25
  17. 2023MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Datasetcited by 25
  18. 2021Learning Hierarchical Attention for Weakly-supervised Chest X-Ray Abnormality Localization and Diagnosiscited by 24
  19. 2023A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?cited by 24
  20. 2024Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-ray Expert Modelscited by 24
  21. 2026Medical SAM3: A Foundation Model for Universal Prompt-Driven Medical Image Segmentationcited by 23
  22. 2022Diffractive all-optical computing for quantitative phase imagingcited by 22
  23. 2021VinDr-SpineXR: A deep learning framework for spinal lesions detection and classification from radiographscited by 19
  24. 2022Evaluating Explainable AI on a Multi-Modal Medical Imaging Task: Can Existing Algorithms Fulfill Clinical Requirements?cited by 19
  25. 2022GATE: Graph CCA for Temporal SElf-supervised Learning for Label-efficient fMRI Analysiscited by 19
  26. 2023Advancing Volumetric Medical Image Segmentation via Global-Local Masked Autoencodercited by 19
  27. 2024Fr'echet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasetscited by 19
  28. 2021VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRIcited by 18
  29. 2022Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structurescited by 18
  30. 2023Synthetic Augmentation with Large-scale Unconditional Pre-trainingcited by 18
  31. 2021BDD4BNN: A BDD-based Quantitative Analysis Framework for Binarized Neural Networkscited by 17
  32. 2024MiM: Mask in Mask Self-Supervised Pre-Training for 3D Medical Image Analysiscited by 17
  33. 2021Domain Composition and Attention for Unseen-Domain Generalizable Medical Image Segmentationcited by 16
  34. 2024A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trendscited by 16
  35. 2021A New Semi-supervised Learning Benchmark for Classifying View and Diagnosing Aortic Stenosis from Echocardiogramscited by 15
  36. 2021Uncertainty-aware GAN with Adaptive Loss for Robust MRI Image Enhancementcited by 15
  37. 2023Zero-shot Causal Graph Extrapolation from Text via LLMscited by 15
  38. 2024The Impact of Scanner Domain Shift on Deep Learning Performance in Medical Imaging: an Experimental Studycited by 15

50 most cited of 399 All 399 (JSON)