SceneAdapt: Scene-based domain adaptation for semantic segmentation using adversarial learning

Mauro, Daniele Di; Furnari, Antonino; Patanè, Giuseppe; Battiato, Sebastiano; Farinella, Giovanni Maria

doi:10.1016/j.patrec.2020.06.002

Cited by 16 publications

(9 citation statements)

References 7 publications

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“…Finally, the morphological opening operation was used to process the prediction results. In future work, we will explore the integration of prior knowledge for the macular edema in retinal OCT images, investigate advanced semantic segmentation network architectures and the self-supervision model [31,32], and achieve much better 3D segmentation results. For the problems of small amount of data and the annotation differences caused by the subjectivity of experts, we will increase the collection of data, find a number of relevant professionals to label, and select relatively accurate annotations to train the network model more effectively.…”

Section: Discussionmentioning

confidence: 99%

Automatic Segmentation of Macular Edema in Retinal OCT Images Using Improved U-Net++

Gao

Wang

2020

Applied Sciences

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The number and volume of retinal macular edemas are important indicators for screening and diagnosing retinopathy. Aiming at the problem that the segmentation method of macular edemas in a retinal optical coherence tomography (OCT) image is not ideal in segmentation of diverse edemas, this paper proposes a new method of automatic segmentation of macular edema regions in retinal OCT images using the improved U-Net++. The proposed method makes full use of the U-Net++ re-designed skip pathways and dense convolution block; reduces the semantic gap of the feature maps in the encoder/decoder sub-network; and adds the improved Resnet network as the backbone, which make the extraction of features in the edema regions more accurate and improves the segmentation effect. The proposed method was trained and validated on the public dataset of Duke University, and the experiments demonstrated the proposed method can not only improve the overall segmentation effect, but also can significantly improve the segmented precision for diverse edema in multi-regions, as well as reducing the error of the number of edema regions.

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Section: Discussionmentioning

confidence: 99%

Automatic Segmentation of Macular Edema in Retinal OCT Images Using Improved U-Net++

Gao

Wang

2020

Applied Sciences

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“…We used A1, A2, and A4 as source domain, i.e., excluding the tobacco plants (as in [18], we named this group of images CVPPP*). Overall, the CVPPP* dataset contains 964 images and a number of leaves ranging in [4,32]. For training, we split this dataset as in [17] to perform a 4-fold cross-validation for the pretraining step.…”

Section: Plantsmentioning

confidence: 99%

“…To minimise the covariate shift, several approaches have been proposed, such as Maximum Mean Discrepancy (MMD) [3], adversarial training [4][5][6][7], as well as styletransfer [8]. DA has recently been mostly investigated for classification tasks, showing outstanding results on closed set [5,7,[9][10][11], open set [12,13], partial [14,15], and even universal cases [16].…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Domain Adaptation for Holistic Counting under Label Gap

et al. 2021

Self Cite

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This paper proposes a novel approach for semi-supervised domain adaptation for holistic regression tasks, where a DNN predicts a continuous value y∈R given an input image x. The current literature generally lacks specific domain adaptation approaches for this task, as most of them mostly focus on classification. In the context of holistic regression, most of the real-world datasets not only exhibit a covariate (or domain) shift, but also a label gap—the target dataset may contain labels not included in the source dataset (and vice versa). We propose an approach tackling both covariate and label gap in a unified training framework. Specifically, a Generative Adversarial Network (GAN) is used to reduce covariate shift, and label gap is mitigated via label normalisation. To avoid overfitting, we propose a stopping criterion that simultaneously takes advantage of the Maximum Mean Discrepancy and the GAN Global Optimality condition. To restore the original label range—that was previously normalised—a handful of annotated images from the target domain are used. Our experimental results, run on 3 different datasets, demonstrate that our approach drastically outperforms the state-of-the-art across the board. Specifically, for the cell counting problem, the mean squared error (MSE) is reduced from 759 to 5.62; in the case of the pedestrian dataset, our approach lowered the MSE from 131 to 1.47. For the last experimental setup, we borrowed a task from plant biology, i.e., counting the number of leaves in a plant, and we ran two series of experiments, showing the MSE is reduced from 2.36 to 0.88 (intra-species), and from 1.48 to 0.6 (inter-species).

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“…Unlike cross-view image classification [36,63,10,1,16], aligning domains of different viewpoints for pixel-level prediction tasks is ill-posed, since the task is indeed view dependent [7]. The most relevant are [11,8], which again resort to adversarial domain alignment. Additionally, [8] requires known camera intrinsics and extrinsics.…”

Section: Related Workmentioning

confidence: 99%

ADeLA: Automatic Dense Labeling with Attention for Viewpoint Adaptation in Semantic Segmentation

Yang,

Ren,

Wang

et al. 2021

Preprint

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SceneAdapt: Scene-based domain adaptation for semantic segmentation using adversarial learning

Cited by 16 publications

References 7 publications

Automatic Segmentation of Macular Edema in Retinal OCT Images Using Improved U-Net++

Automatic Segmentation of Macular Edema in Retinal OCT Images Using Improved U-Net++

Semi-Supervised Domain Adaptation for Holistic Counting under Label Gap

ADeLA: Automatic Dense Labeling with Attention for Viewpoint Adaptation in Semantic Segmentation

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