AMD Severity Prediction And Explainability Using Image Registration And Deep Embedded Clustering
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Abstract (arXiv)
We propose a method to predict severity of age related macular degeneration (AMD) from input optical coherence tomography (OCT) images. Although there is no standard clinical severity scale for AMD, we leverage deep learning (DL) based image registration and clustering methods to identify diseased cases and predict their severity. Experiments demonstrate our approach's disease classification performance matches state of the art methods. The predicted disease severity performs well on previously unseen data. Registration output provides better explainability than class activation maps regarding label and severity decisions
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jul 2019.
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Citing papers per year: 2021: 4; 2022: 4.
8 papers.
- 2021Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentationcited by 22
- 2021CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysiscited by 8
- 2021Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentationcited by 5
- 2022Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classificationcited by 5
- 2021Multimodal Generalized Zero Shot Learning for Gleason Grading using Self-Supervised Learningcited by 4
- 2022Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classificationcited by 2
- 2022Improved Super Resolution of MR Images Using CNNs and Vision Transformerscited by 1
- 2022Cyclic Generative Adversarial Networks With Congruent Image-Report Generation For Explainable Medical Image Analysiscited by 0
- 2022Cyclic Generative Adversarial Networks With Congruent Image-Report Generation For Explainable Medical Image Analysis16 Nov 2022
- 2022Improved Super Resolution of MR Images Using CNNs and Vision Transformers24 Jul 2022
- 2022Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classification27 Jun 2022
- 2022Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification4 Apr 2022
- 2021Multimodal Generalized Zero Shot Learning for Gleason Grading using Self-Supervised Learning15 Nov 2021
- 2021Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentation1 Oct 2021
- 2021CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysis15 Jun 2021
- 2021Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentation13 Apr 2021