Incremental Cross-Domain Adaptation for Robust Retinopathy Screening via
Bayesian Deep Learning
- URL: http://arxiv.org/abs/2110.09319v1
- Date: Mon, 18 Oct 2021 13:45:21 GMT
- Title: Incremental Cross-Domain Adaptation for Robust Retinopathy Screening via
Bayesian Deep Learning
- Authors: Taimur Hassan and Bilal Hassan and Muhammad Usman Akram and Shahrukh
Hashmi and Abdel Hakim Taguri and Naoufel Werghi
- Abstract summary: Retinopathy represents a group of retinal diseases that, if not treated timely, can cause severe visual impairments or even blindness.
This paper presents a novel incremental cross-domain adaptation instrument that allows any deep classification model to progressively learn abnormal retinal pathologies.
The proposed framework, evaluated on six public datasets, outperforms the state-of-the-art competitors by achieving an overall accuracy and F1 score of 0.9826 and 0.9846, respectively.
- Score: 7.535751594024775
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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