scholarly journals Optimized-Unet: Novel Algorithm for Parapapillary Atrophy Segmentation

2021 ◽  
Vol 15 ◽  
Author(s):  
Cheng Wan ◽  
Jiasheng Wu ◽  
Han Li ◽  
Zhipeng Yan ◽  
Chenghu Wang ◽  
...  
Keyword(s):  
Loss Function ◽  
High Myopia ◽  
Area Under Curve ◽  
Multi Scale ◽  
Retinal Pathology ◽  
Segmentation Accuracy ◽  
Parapapillary Atrophy ◽  
Contour Information ◽  
Novel Algorithm ◽  
Visual Geometry

In recent years, an increasing number of people have myopia in China, especially the younger generation. Common myopia may develop into high myopia. High myopia causes visual impairment and blindness. Parapapillary atrophy (PPA) is a typical retinal pathology related to high myopia, which is also a basic clue for diagnosing high myopia. Therefore, accurate segmentation of the PPA is essential for high myopia diagnosis and treatment. In this study, we propose an optimized Unet (OT-Unet) to solve this important task. OT-Unet uses one of the pre-trained models: Visual Geometry Group (VGG), ResNet, and Res2Net, as a backbone and is combined with edge attention, parallel partial decoder, and reverse attention modules to improve the segmentation accuracy. In general, using the pre-trained models can improve the accuracy with fewer samples. The edge attention module extracts contour information, the parallel partial decoder module combines the multi-scale features, and the reverse attention module integrates high- and low-level features. We also propose an augmented loss function to increase the weight of complex pixels to enable the network to segment more complex lesion areas. Based on a dataset containing 360 images (Including 26 pictures provided by PALM), the proposed OT-Unet achieves a high AUC (Area Under Curve) of 0.9235, indicating a significant improvement over the original Unet (0.7917).

scholarly journals Frequency-domain loss function for deep exposure correction of dark images

2021 ◽  
Author(s):  
Ojasvi Yadav ◽  
Koustav Ghosal ◽  
Sebastian Lutz ◽  
Aljosa Smolic
Keyword(s):  
Loss Function ◽  
Correct Choice ◽  
Frequency Space ◽  
Multi Scale ◽  
Quantitative Metrics ◽  
Image Translation ◽  
Domain Loss ◽  
In The Wild ◽  
End To End ◽  
Classical Image

AbstractWe address the problem of exposure correction of dark, blurry and noisy images captured in low-light conditions in the wild. Classical image-denoising filters work well in the frequency space but are constrained by several factors such as the correct choice of thresholds and frequency estimates. On the other hand, traditional deep networks are trained end to end in the RGB space by formulating this task as an image translation problem. However, that is done without any explicit constraints on the inherent noise of the dark images and thus produces noisy and blurry outputs. To this end, we propose a DCT/FFT-based multi-scale loss function, which when combined with traditional losses, trains a network to translate the important features for visually pleasing output. Our loss function is end to end differentiable, scale-agnostic and generic; i.e., it can be applied to both RAW and JPEG images in most existing frameworks without additional overhead. Using this loss function, we report significant improvements over the state of the art using quantitative metrics and subjective tests.

scholarly journals HR-MPF: high-resolution representation network with multi-scale progressive fusion for pulmonary nodule segmentation and classification

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Ling Zhu ◽  
Hongqing Zhu ◽  
Suyi Yang ◽  
Pengyu Wang ◽  
Yang Yu
Keyword(s):  
High Resolution ◽  
Loss Function ◽  
Pulmonary Nodule ◽  
Feature Fusion ◽  
Back Propagation ◽  
Pulmonary Nodules ◽  
Improve Patient Survival ◽  
Multi Scale ◽  
Training Scheme ◽  
Adversarial Training

AbstractAccurate segmentation and classification of pulmonary nodules are of great significance to early detection and diagnosis of lung diseases, which can reduce the risk of developing lung cancer and improve patient survival rate. In this paper, we propose an effective network for pulmonary nodule segmentation and classification at one time based on adversarial training scheme. The segmentation network consists of a High-Resolution network with Multi-scale Progressive Fusion (HR-MPF) and a proposed Progressive Decoding Module (PDM) recovering final pixel-wise prediction results. Specifically, the proposed HR-MPF firstly incorporates boosted module to High-Resolution Network (HRNet) in a progressive feature fusion manner. In this case, feature communication is augmented among all levels in this high-resolution network. Then, downstream classification module would identify benign and malignant pulmonary nodules based on feature map from PDM. In the adversarial training scheme, a discriminator is set to optimize HR-MPF and PDM through back propagation. Meanwhile, a reasonably designed multi-task loss function optimizes performance of segmentation and classification overall. To improve the accuracy of boundary prediction crucial to nodule segmentation, a boundary consistency constraint is designed and incorporated in the segmentation loss function. Experiments on publicly available LUNA16 dataset show that the framework outperforms relevant advanced methods in quantitative evaluation and visual perception.

scholarly journals A NEPHOGRAM PREDICTION METHOD BASED ON GENERATIVE ADVERSARIAL NETWORK

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 925-930
Author(s):  
Y. Xun ◽  
W. Q. Yu
Keyword(s):  
Loss Function ◽  
Internal Representation ◽  
Prediction Method ◽  
Ground Truth ◽  
Generative Adversarial Network ◽  
Multi Scale ◽  
Adversarial Network ◽  
Basic Characteristics ◽  
Cloud Evolution

Abstract. As one of the important sources of meteorological information, satellite nephogram is playing an increasingly important role in the detection and forecast of disastrous weather. The predictions about the movement and transformation of cloud with certain timeliness can enhance the practicability of satellite nephogram. Based on the generative adversarial network in unsupervised learning, we propose a prediction model of time series nephogram, which construct the internal representation of cloud evolution accurately and realize nephogram prediction for the next several hours. We improve the traditional generative adversarial network by constructing the generator and discriminator used the multi-scale convolution network. After the scale transform process, different scales operate convolutions in parallel and then merge the features. This structure can solve the problem of long-term dependence in the traditional network, and both global and detailed features are considered. Then according to the network structure and practical application, we define a new loss function combined with adversarial loss function to accelerate the convergence of model and sharpen predictions which keeps the effectivity of predictions further. Our method has no need to carry out the stack mathematics calculation and the manual operations, has greatly enhanced the feasibility and the efficiency. The results show that this model can reasonably describe the basic characteristics and evolution trend of cloud cluster, the prediction nephogram has very high similarity to the ground-truth nephogram.

scholarly journals MF-Net: Multi-Scale Information Fusion Network for CNV Segmentation in Retinal OCT Images

2021 ◽  
Vol 15 ◽  
Author(s):  
Qingquan Meng ◽  
Lianyu Wang ◽  
Tingting Wang ◽  
Meng Wang ◽  
Weifang Zhu ◽  
...  
Keyword(s):  
Information Fusion ◽  
Data Augmentation ◽  
Contextual Information ◽  
Treatment Plan ◽  
Speckle Noise ◽  
Medical Image Segmentation ◽  
Unlabeled Data ◽  
Feature Maps ◽  
Multi Scale ◽  
Segmentation Accuracy

Choroid neovascularization (CNV) is one of the blinding ophthalmologic diseases. It is mainly caused by new blood vessels growing in choroid and penetrating Bruch's membrane. Accurate segmentation of CNV is essential for ophthalmologists to analyze the condition of the patient and specify treatment plan. Although many deep learning-based methods have achieved promising results in many medical image segmentation tasks, CNV segmentation in retinal optical coherence tomography (OCT) images is still very challenging as the blur boundary of CNV, large morphological differences, speckle noise, and other similar diseases interference. In addition, the lack of pixel-level annotation data is also one of the factors that affect the further improvement of CNV segmentation accuracy. To improve the accuracy of CNV segmentation, a novel multi-scale information fusion network (MF-Net) based on U-Shape architecture is proposed for CNV segmentation in retinal OCT images. A novel multi-scale adaptive-aware deformation module (MAD) is designed and inserted into the top of the encoder path, aiming at guiding the model to focus on multi-scale deformation of the targets, and aggregates the contextual information. Meanwhile, to improve the ability of the network to learn to supplement low-level local high-resolution semantic information to high-level feature maps, a novel semantics-details aggregation module (SDA) between encoder and decoder is proposed. In addition, to leverage unlabeled data to further improve the CNV segmentation, a semi-supervised version of MF-Net is designed based on pseudo-label data augmentation strategy, which can leverage unlabeled data to further improve CNV segmentation accuracy. Finally, comprehensive experiments are conducted to validate the performance of the proposed MF-Net and SemiMF-Net. The experiment results show that both proposed MF-Net and SemiMF-Net outperforms other state-of-the-art algorithms.

Multi-Scale Collaborative Network for Human Pose Estimation

2019 ◽  
Vol 16 (04) ◽  
pp. 1941003
Author(s):  
Chunsheng Guo ◽  
Jialuo Zhou ◽  
Wenlong Du ◽  
Xuguang Zhang
Keyword(s):  
Loss Function ◽  
Pose Estimation ◽  
Large Scale ◽  
Human Pose Estimation ◽  
Small Scale ◽  
Feature Maps ◽  
Multi Scale ◽  
Human Pose ◽  
Processing Network ◽  
Weight Coefficients

Human pose estimation is a fundamental but challenging task in computer vision. The estimation of human pose mainly depends on the global information of the keypoint type and the local information of the keypoint location. However, the consistency of the cascading process makes it difficult for each stacking network to form a differentiation and collaboration mechanism. In order to solve these problems, this paper introduces a new human pose estimation framework called Multi-Scale Collaborative (MSC) network. The pre-processing network forms feature maps of different sizes, and dispatches them to various locations of the stack network, with small-scale features reaching the front-end stacking network and large-scale features reaching the back-end stacking network. A new loss function is proposed for MSC network. Different keypoints have different weight coefficients of loss function at different scales, and the keypoint weight coefficients are dynamically adjusted from the top hourglass network to the bottom hourglass network. Experimental results show that the proposed method is competitive in MPII and LSP challenge leaderboard among the state-of-the-art methods.

Automatic Generation of Pencil Drawing from Digital Image

2010 ◽  
Vol 159 ◽  
pp. 481-486
Author(s):  
Yao Li
Keyword(s):  
Digital Image ◽  
Building Blocks ◽  
Automatic Generation ◽  
Experimental Results ◽  
Original Image ◽  
Edge Detector ◽  
Visual Effects ◽  
Multi Scale ◽  
Pencil Drawing ◽  
Novel Algorithm

This paper proposes a novel algorithm for the automatic generation of pencil drawing from digital image. We use multi-scale edge detector to construct an outline map according to the structural importance. Using strokes as the major building blocks, we smooth the boundary and rendering outline according to the pencil sketch style. Then, tone rendering is carried out to convey both the tone and textures of the original image. Experimental results on natural scenery and architectural scenery images show that the pencil sketch drawings produced by our method are of good visual effects. Besides, the implementation require less than 1 second on the image with 400*300 pixels.

scholarly journals Multi-scale hippocampal parcellation improves atlas-based segmentation accuracy

2017 ◽  
Author(s):  
Andrew J. Plassard ◽  
Maureen McHugo ◽  
Stephan Heckers ◽  
Bennett A. Landman
Keyword(s):  
Multi Scale ◽  
Segmentation Accuracy

scholarly journals Adaptive Feature Pyramid Network to Predict Crisp Boundaries via NMS Layer and ODS F-Measure Loss Function

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 32
Author(s):  
Gang Sun ◽  
Hancheng Yu ◽  
Xiangtao Jiang ◽  
Mingkui Feng
Keyword(s):  
Edge Detection ◽  
Loss Function ◽  
State Of The Art ◽  
Cross Entropy ◽  
Post Processing ◽  
Multi Scale ◽  
Feature Pyramid ◽  
Multi Level ◽  
Different Levels ◽  
F Measure

Edge detection is one of the fundamental computer vision tasks. Recent methods for edge detection based on a convolutional neural network (CNN) typically employ the weighted cross-entropy loss. Their predicted results being thick and needing post-processing before calculating the optimal dataset scale (ODS) F-measure for evaluation. To achieve end-to-end training, we propose a non-maximum suppression layer (NMS) to obtain sharp boundaries without the need for post-processing. The ODS F-measure can be calculated based on these sharp boundaries. So, the ODS F-measure loss function is proposed to train the network. Besides, we propose an adaptive multi-level feature pyramid network (AFPN) to better fuse different levels of features. Furthermore, to enrich multi-scale features learned by AFPN, we introduce a pyramid context module (PCM) that includes dilated convolution to extract multi-scale features. Experimental results indicate that the proposed AFPN achieves state-of-the-art performance on the BSDS500 dataset (ODS F-score of 0.837) and the NYUDv2 dataset (ODS F-score of 0.780).

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