CFPNet-M: A Light-Weight Encoder-Decoder Based Network for Multimodal Biomedical Image Real-Time Segmentation

Lou, Ange; Guan, Shuyue; Loew, Murray H.

doi:10.48550/arxiv.2105.04075

Cited by 5 publications

(6 citation statements)

References 46 publications

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“…The application of the newest U-shaped models, basic U-Net by Ronnenberger et al [ 66 ], DC-UNet [ 71 ], and CFPNet-M [ 72 ], to epidermis and SLEB segmentation can be found in [ 48 ]. The authors analyzed the influence of the size of the images used for network training, augmentation technique, optimization method, region of interest (ROI) selection, and binarization threshold on the final segmentation accuracy.…”

Section: Computer-aided Diagnosis Methodsmentioning

confidence: 99%

Computer-Aided Diagnosis Methods for High-Frequency Ultrasound Data Analysis: A Review

Czajkowska

Borak

2022

Sensors

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Over the last few decades, computer-aided diagnosis systems have become a part of clinical practice. They have the potential to assist clinicians in daily diagnostic tasks. The image processing techniques are fast, repeatable, and robust, which helps physicians to detect, classify, segment, and measure various structures. The recent rapid development of computer methods for high-frequency ultrasound image analysis opens up new diagnostic paths in dermatology, allergology, cosmetology, and aesthetic medicine. This paper, being the first in this area, presents a research overview of high-frequency ultrasound image processing techniques, which have the potential to be a part of computer-aided diagnosis systems. The reviewed methods are categorized concerning the application, utilized ultrasound device, and image data-processing type. We present the bridge between diagnostic needs and already developed solutions and discuss their limitations and future directions in high-frequency ultrasound image analysis. A search was conducted of the technical literature from 2005 to September 2022, and in total, 31 studies describing image processing methods were reviewed. The quantitative and qualitative analysis included 39 algorithms, which were selected as the most effective in this field. They were completed by 20 medical papers and define the needs and opportunities for high-frequency ultrasound application and CAD development.

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Section: Computer-aided Diagnosis Methodsmentioning

confidence: 99%

Computer-Aided Diagnosis Methods for High-Frequency Ultrasound Data Analysis: A Review

Czajkowska

Borak

2022

Sensors

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“…Furthermore, publications inspired by the original U-Net architecture continue to appear. Colman et al [26] introduced a deep residual bottleneck to the U-Net, and Ange Lou [27] added the dilated channel-wise CNN module and simplified the U-shaped layout in order to get a lightweight but efficient model.…”

Section: Improving Semantic Segmentationmentioning

confidence: 99%

“…It implements a feature pyramid channel to a U-shaped architecture. It is expected to show competitive performances with great advantages of much fewer parameters and smaller model file size [27].…”

Section: G Cfpnet-mmentioning

confidence: 99%

CSU-Net: Contour Semantic Segmentation Self-Enhancement for Human Head Detection

Chouai

Doležel

2023

IEEE Access

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The computer vision community has made tremendous progress in solving a variety of semantic image understanding tasks, such as classification and segmentation. With the advancement of imaging technology and hardware, image semantic segmentation, through the use of deep learning, is among the most common topics which have been worked on in the last decade. However, image semantic segmentation suffers from several drawbacks such as insufficient detection of object boundaries. In this study, we present a new convolutional neural network architecture called CSU-Net that aims to self-enhance the results of semantic segmentation. The proposed model consists of two strongly concatenated encoder-decoder blocks. With this design, we reduced requirements on computing power and memory size to decrease costs and increase the training/prediction speed. This study also demonstrates the advantage of the proposed system for small training data sets. The proposed approach has been implemented on our private dataset, as well as on a publicly available dataset. A comparative analysis was carried out with four popular segmentation models and three other recently introduced architectures to show the efficiency of the proposed system. CSU-Net outperformed the other competing neural networks that we considered for the comparative study. As an example, it succeeded in improving the traditional U-Net result by approximately 50% in mean Intersection over Union (mIoU) for both tested datasets. Based on our experience, the CSU-Net can improve results of semantic segmentation in many applications.

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“…In addition, their receptive fields are usually small and do not perform well in datasets with sharply varying object sizes [33]. Alternatively, our previous works [33,34] proposed a lightweight Channel-wise Feature Pyramid (CFP) module and successfully applied it to both nature and medical image segmentation. The architecture of this CFP module is shown in Figure 3.…”

Section: Channel-wise Feature Pyramid Modulementioning

confidence: 99%

CaraNet: context axial reverse attention network for segmentation of small medical objects

Lou

Guan²,

Ko³

et al. 2022

Medical Imaging 2022: Image Processing

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Purpose: Segmenting medical images accurately and reliably is important for disease diagnosis and treatment. It is a challenging task because of the wide variety of objects' sizes, shapes, and scanning modalities. Recently, many convolutional neural networks (CNN) have been designed for segmentation tasks and achieved great success. Few studies, however, have fully considered the sizes of objects, and thus most demonstrate poor performance for small objects segmentation. This can have a significant impact on the early detection of diseases. Approach: This paper proposes a Context Axial Reverse Attention Network (CaraNet) to improve the segmentation performance on small objects compared with several recent state-of-the-art models. CaraNet applies axial reserve attention (ARA) and channel-wise feature pyramid (CFP) module to dig feature information of small medical object. And we evaluate our model by six different measurement metrics. Results: We test our CaraNet on brain tumor (BraTS 2018) and polyp (Kvasir-SEG, CVC-ColonDB, CVC-ClinicDB, CVC-300, and ETIS-LaribPolypDB) segmentation datasets. Our CaraNet achieves the top-rank mean Dice segmentation accuracy, and results show a distinct advantage of CaraNet in the segmentation of small medical objects. Conclusion:We proposed CaraNet to segment small medical objects and outperform other state-of-the-art methods.

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CFPNet-M: A Light-Weight Encoder-Decoder Based Network for Multimodal Biomedical Image Real-Time Segmentation

Cited by 5 publications

References 46 publications

Computer-Aided Diagnosis Methods for High-Frequency Ultrasound Data Analysis: A Review

Computer-Aided Diagnosis Methods for High-Frequency Ultrasound Data Analysis: A Review

CSU-Net: Contour Semantic Segmentation Self-Enhancement for Human Head Detection

CaraNet: context axial reverse attention network for segmentation of small medical objects

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