# Incremental Cross-Domain Adaptation for Robust Retinopathy Screening via Bayesian Deep Learning

arXiv: https://arxiv.org/abs/2110.09319 · HTML: https://mlsanity.com/paper/2110.09319/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 95 references that are in the map.
- First posted on arXiv 18 Oct 2021.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Efficient Lifelong Learning with A-GEM](https://mlsanity.com/paper/1812.00420/) (2018)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence](https://mlsanity.com/paper/1801.10112/) (2018)
- [FDA: Fourier Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2004.05498/) (2020)
- [Class-incremental Learning via Deep Model Consolidation](https://mlsanity.com/paper/1903.07864/) (2019)
- [Incremental Learning Through Deep Adaptation](https://mlsanity.com/paper/1705.04228/) (2017)
- [A Survey of Deep Learning for Scientific Discovery](https://mlsanity.com/paper/2003.11755/) (2020)
- [Learn to Segment Retinal Lesions and Beyond](https://mlsanity.com/paper/1912.11619/) (2019)
- [Clinically Verified Hybrid Deep Learning System for Retinal Ganglion Cells Aware Grading of Glaucomatous Progression](https://mlsanity.com/paper/2010.03872/) (2020)
- [Cascaded Structure Tensor Framework for Robust Identification of Heavily Occluded Baggage Items from X-ray Scans](https://mlsanity.com/paper/2004.06780/) (2020)
- [SIP-SegNet: A Deep Convolutional Encoder-Decoder Network for Joint Semantic Segmentation and Extraction of Sclera, Iris and Pupil based on Periocular Region Suppression](https://mlsanity.com/paper/2003.00825/) (2020)
- [Unsupervised Anomaly Instance Segmentation for Baggage Threat Recognition](https://mlsanity.com/paper/2107.07333/) (2021)
- [Max-margin Class Imbalanced Learning with Gaussian Affinity](https://mlsanity.com/paper/1901.07711/) (2019)
- [Trainable Structure Tensors for Autonomous Baggage Threat Detection Under Extreme Occlusion](https://mlsanity.com/paper/2009.13158/) (2020)
- [A Dilated Residual Hierarchically Fashioned Segmentation Framework for Extracting Gleason Tissues and Grading Prostate Cancer from Whole Slide Images](https://mlsanity.com/paper/2011.00527/) (2020)
- [Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning](https://mlsanity.com/paper/1810.10342/) (2018)
- [Tensor Pooling Driven Instance Segmentation Framework for Baggage Threat Recognition](https://mlsanity.com/paper/2108.09603/) (2021)
- [Bridge the Domain Gap Between Ultra-wide-field and Traditional Fundus Images via Adversarial Domain Adaptation](https://mlsanity.com/paper/2003.10042/) (2020)
- [Automated segmentation and extraction of posterior eye segment using OCT scans](https://mlsanity.com/paper/2109.10000/) (2021)

## Built on by (most cited first, 3 of 3)

- [Temporal Fusion Based Mutli-scale Semantic Segmentation for Detecting Concealed Baggage Threats](https://mlsanity.com/paper/2111.02651/) (2021)
- [UT-Net: Combining U-Net and Transformer for Joint Optic Disc and Cup Segmentation and Glaucoma Detection](https://mlsanity.com/paper/2303.04939/) (2023)
- [Integrating Features for Recognizing Human Activities through Optimized Parameters in Graph Convolutional Networks and Transformer Architectures](https://mlsanity.com/paper/2408.16442/) (2024)

## Abstract (arXiv)

Retinopathy represents a group of retinal diseases that, if not treated timely, can cause severe visual impairments or even blindness. Many researchers have developed autonomous systems to recognize retinopathy via fundus and optical coherence tomography (OCT) imagery. However, most of these frameworks employ conventional transfer learning and fine-tuning approaches, requiring a decent amount of well-annotated training data to produce accurate diagnostic performance. This paper presents a novel incremental cross-domain adaptation instrument that allows any deep classification model to progressively learn abnormal retinal pathologies in OCT and fundus imagery via few-shot training. Furthermore, unlike its competitors, the proposed instrument is driven via a Bayesian multi-objective function that not only enforces the candidate classification network to retain its prior learned knowledge during incremental training but also ensures that the network understands the structural and semantic relationships between previously learned pathologies and newly added disease categories to effectively recognize them at the inference stage. The proposed framework, evaluated on six public datasets acquired with three different scanners to screen thirteen retinal pathologies, outperforms the state-of-the-art competitors by achieving an overall accuracy and F1 score of 0.9826 and 0.9846, respectively.