Instance Segmentation Model Created from Three Semantic Segmentations of Mask, Boundary and Centroid Pixels Verified on GlaS Dataset

Malík, Peter; Kristofik, Stefan; Knapova, Kristina

doi:10.15439/2020f175

Cited by 10 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, fully convolutional networks (FCN) 48 have been used for image-to-pixel-level classification to reduce the computational workload of pre-processing. Guerrero-Pena et al 49 presented a weighted map for a weighted cross-entropy loss function used for imbalanced parametric correction, while Chen et al 50 proposed a contour-aware FCN for the segmentation of medical part images.…”

Section: Related Workmentioning

confidence: 99%

White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learning

Rashid,

Raza,

Sharif

et al. 2023

Sci Rep

View full text Add to dashboard Cite

White blood cells (WBCs) are an indispensable constituent of the immune system. Efficient and accurate categorization of WBC is a critical task for disease diagnosis by medical experts. This categorization helps in the correct identification of medical problems. In this research work, WBC classes are categorized with the help of a transform learning model in combination with our proposed virtual hexagonal trellis (VHT) structure feature extraction method. The VHT feature extractor is a kernel-based filter model designed over a square lattice. In the first step, Graft Net CNN model is used to extract features of augmented data set images. Later, the VHT base feature extractor extracts useful features. The CNN-extracted features are passed to ant colony optimization (ACO) module for optimal features acquisition. Extracted features from the VHT base filter and ACO are serially merged to create a single feature vector. The merged features are passed to the support vector machine (SVM) variants for optimal classification. Our strategy yields 99.9% accuracy, which outperforms other existing methods.

show abstract

Section: Related Workmentioning

confidence: 99%

White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learning

Rashid,

Raza,

Sharif

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…To verify the effectiveness and efficiency of our TransAt-tUnet, we first conducted comparative experiments for the tasks of skin lesion segmentation on ISIC-2018 [32] dataset, and lung field segmentation on the combination of the JSRT [33], Montgomery [34], and NIH [23] datasets. Moreover, the proposed TransAttUnet is also evaluated on the Clean-CC-CCII dataset [35], 2018 Data Science Bowl (Bowl) dataset [36], and the Gland Segmentation (GlaS) dataset [22], respectively. Notably, it is more rigorous and challenging to solve these tasks.…”

Section: A Datasetsmentioning

confidence: 99%

TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentation

Chen¹,

Liu²,

Zhang³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Much effort has gone into research on image segmentation in recent years and great progress has been made [2], [3], [4], [5], [6]. Despite this, segmentation still remains a difficult problem because of rich intra-class variation, context variation and ambiguities resulting from the low resolution of images.…”

Section: Introductionmentioning

confidence: 99%

Urban scene semantic segmentation using the U-Net model

Ciecholewski

2023

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

Vision-based semantic segmentation of complex urban street scenes is a very important function during autonomous driving (AD), which will become an important technology in industrialized countries in the near future. Today, advanced driver assistance systems (ADAS) improve traffic safety thanks to the application of solutions that enable detecting objects, recognising road signs, segmenting the road, etc. The basis for these functionalities is the adoption of various classifiers. This publication presents solutions utilising convolutional neural networks, such as MobileNet and ResNet50, which were used as encoders in the U-Net model to semantically segment images of complex urban scenes taken from the publicly available Cityscapes dataset. Some modifications of the encoder/decoder architecture of the U-Net model were also proposed and the result was named the MU-Net. During tests carried out on 500 images, the MU-Net model produced slightly better segmentation results than the universal MobileNet and ResNet networks, as measured by the Jaccard index, which amounted to 88.85%. The experiments showed that the MobileNet network had the best ratio of accuracy to the number of parameters used and at the same time was the least sensitive to unusual phenomena occurring in images.

show abstract

Instance Segmentation Model Created from Three Semantic Segmentations of Mask, Boundary and Centroid Pixels Verified on GlaS Dataset

Cited by 10 publications

References 24 publications

White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learning

White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learning

TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentation

Urban scene semantic segmentation using the U-Net model

Contact Info

Product

Resources

About