mlsanityData to 7 Oct 2026

AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics

arXiv 2110.10332 ↗First posted 20 Oct 2021physics.med-ph cs.CV eess.IVcited by 2

Not on the map. Why?

Abstract (arXiv)

Artificial intelligence (AI) techniques have significant potential to enable effective, robust and automated image phenotyping including identification of subtle patterns. AI-based detection searches the image space to find the regions of interest based on patterns and features. There is a spectrum of tumor histologies from benign to malignant that can be identified by AI-based classification approaches using image features. The extraction of minable information from images gives way to the field of radiomics and can be explored via explicit (handcrafted/engineered) and deep radiomics frameworks. Radiomics analysis has the potential to be utilized as a noninvasive technique for the accurate characterization of tumors to improve diagnosis and treatment monitoring. This work reviews AI-based techniques, with a special focus on oncological PET and PET/CT imaging, for different detection, classification, and prediction/prognosis tasks. We also discuss needed efforts to enable the translation of AI techniques to routine clinical workflows, and potential improvements and complementary techniques such as the use of natural language processing on electronic health records and neuro-symbolic AI techniques.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 32 of its 195 references that are in the map. First posted on arXiv 20 Oct 2021.

Builds on

32 of 195 references.

References · 32

  1. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  2. 2016Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotationscited by 2,646
  3. 2015Learning Deep Features for Discriminative Localizationcited by 2,430
  4. 2018ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnesscited by 1,430
  5. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
  6. 2019Neural Probabilistic Logic Programming in DeepProbLogcited by 389
  7. 2016DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Networkcited by 354
  8. 2020Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Makingcited by 281
  9. 2015A Critical Review of Recurrent Neural Networks for Sequence Learningcited by 223
  10. 2016Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learningcited by 209
  11. 2019Deep learning with noisy labels: exploring techniques and remedies in medical image analysiscited by 173
  12. 2016Image biomarker standardisation initiativecited by 61
20 more
  1. 2017Thinking Fast and Slow: Optimization Decomposition Across Timescalescited by 60
  2. 2020FocalMix: Semi-Supervised Learning for 3D Medical Image Detectioncited by 52
  3. 2021Shape or Texture: Understanding Discriminative Features in CNNscited by 52
  4. 2020An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localizationcited by 43
  5. 2018DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classificationcited by 36
  6. 2017Radiomics strategies for risk assessment of tumour failure in head-and-neck cancercited by 31
  7. 2018Cross-Modality Synthesis from CT to PET using FCN and GAN Networks for Improved Automated Lesion Detectioncited by 29
  8. 2017Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networkscited by 26
  9. 2015Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregationcited by 22
  10. 2018Attention-Gated Networks for Improving Ultrasound Scan Plane Detectioncited by 22
  11. 2017Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodulescited by 20
  12. 2017Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)cited by 18
  13. 2018From Hand-Crafted to Deep Learning-based Cancer Radiomics: Challenges and Opportunitiescited by 18
  14. 2018Co-Learning Feature Fusion Maps from PET-CT Images of Lung Cancercited by 14
  15. 2020Lesion Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scalecited by 11
  16. 2021One Map Does Not Fit All: Evaluating Saliency Map Explanation on Multi-Modal Medical Imagescited by 11
  17. 2019Artificial Intelligence in Glioma Imaging: Challenges and Advancescited by 10
  18. 2017ConvNet-Based Localization of Anatomical Structures in 3D Medical Imagescited by 9
  19. 2021A brief history of AI: how to prevent another winter (a critical review)cited by 8
  20. 2017Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT imagescited by 6

Built on by

Cited by year

Citing papers per year: 2022: 2.

2 papers.

  1. 2022Tensor Radiomics: Paradigm for Systematic Incorporation of Multi-Flavoured Radiomics Featurescited by 1
  2. 2022Multi-Task Multi-Scale Learning For Outcome Prediction in 3D PET Imagescited by 0