Semi-Supervised Nests of Melanocytes Segmentation Method Using Convolutional Autoencoders

Kucharski, Dariusz; Kłeczek, Paweł; Jaworek-Korjakowska, Joanna; Dyduch, Grzegorz; Gorgon, Marek Boguslaw

doi:10.3390/s20061546

Cited by 28 publications

(15 citation statements)

References 50 publications

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“…Vununu et al proposed a deep feature extraction method for HEp-2 cell image classification [17]. Kucharski et al proposed a semi-supervised segmentation method to solve the problem of ground-truth images segmentation for the detection of nests of nevus cells in histopathological images of skin specimens [18]. The aforementioned studies did not focus on micronucleus detection.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection Method for Cancer Cell Nucleus Image Based on Deep-Learning Analysis and Color Layer Signature Analysis Algorithm

Pan

et al. 2020

Sensors

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Exploring strategies to treat cancer has always been an aim of medical researchers. One of the available strategies is to use targeted therapy drugs to make the chromosomes in cancer cells unstable such that cell death can be induced, and the elimination of highly proliferative cancer cells can be achieved. Studies have reported that the mitotic defects and micronuclei in cancer cells can be used as biomarkers to evaluate the instability of the chromosomes. Researchers use these two biomarkers to assess the effects of drugs on eliminating cancer cells. However, manual work is required to count the number of cells exhibiting mitotic defects and micronuclei either directly from the viewing window of a microscope or from an image, which is tedious and creates errors. Therefore, this study aims to detect cells with mitotic defects and micronuclei by applying an approach that can automatically count the targets. This approach integrates the application of a convolutional neural network for normal cell identification and the proposed color layer signature analysis (CLSA) to spot cells with mitotic defects and micronuclei. This approach provides a method for researchers to detect colon cancer cells in an accurate and time-efficient manner, thereby decreasing errors and the processing time. The following sections will illustrate the methodology and workflow design of this study, as well as explain the practicality of the experimental comparisons and the results that were used to validate the practicality of this algorithm.

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Section: Introductionmentioning

confidence: 99%

Automatic Detection Method for Cancer Cell Nucleus Image Based on Deep-Learning Analysis and Color Layer Signature Analysis Algorithm

Pan

et al. 2020

Sensors

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“…These representative features enhance the performance of the ANN in the diagnosis of doubtful skin lesions. Skin lesion asymmetry is a strong and efficient feature in the differentiation between benign and malign skin lesions [ 22 ], i.e., the asymmetric degree of a skin lesion is an intuitive mark of its deadly potential. Shape asymmetry mathematically models the human observation of a lesion and correlates it to the ABCD rule for lesion classification.…”

Section: Related Workmentioning

confidence: 99%

“…ANNs have the potential to predict the medical outcome of different kinds of skin lesions. ANNs process data sequentially through a series of layers and aggregate large-scale datasets for training/learning purposes [ 20 , 21 , 22 ]. A CAD system conceived to discriminate melanoma from nevus based on handcraft ABCDE features using a Mutual Information metric was proposed for a binary classification decision [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Towards Accurate Diagnosis of Skin Lesions Using Feedforward Back Propagation Neural Networks

et al. 2021

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In the automatic detection framework, there have been many attempts to develop models for real-time melanoma detection. To effectively discriminate benign and malign skin lesions, this work investigates sixty different architectures of the Feedforward Back Propagation Network (FFBPN), based on shape asymmetry for an optimal structural design that includes both the hidden neuron number and the input data selection. The reason for the choice of shape asymmetry was based on the 5–10% disagreement between dermatologists regarding the efficacy of asymmetry in the diagnosis of malignant melanoma. Asymmetry is quantified based on lesion shape (contour), moment of inertia of the lesion shape and histograms. The FFBPN has a high architecture flexibility, which indicates it as a favorable tool to avoid the over-parameterization of the ANN and, equally, to discard those redundant input datasets that usually result in poor test performance. The FFBPN was tested on four public image datasets containing melanoma, dysplastic nevus and nevus images. Experimental results on multiple benchmark data sets demonstrate that asymmetry A2 is a meaningful feature for skin lesion classification, and FFBPN with 16 neurons in the hidden layer can model the data without compromising prediction accuracy.

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“…Chen et al [14] demonstrated in the PAS-CAL VOC 2012 challenge that the DeepLabv3+ encoderdecoder model yields a state-of-the-art performance [19]. Later we combine it with the decoder part which is known as DUpsample [54], the network is shown in Fig 4. This is because its encoder module can capture rich contextual information from several parallel atrous convolution layers with different rates and its decoder module is able to recover effectively missing boundaries caused by the pooling or convolutions with striding operations in the encoder module [31], [34]. The main advantage of the new upsampling layer lies in that with a relatively lower resolution feature map [54].…”

Section: B Feature Extraction Using a Multi-model Deep Encoder-decodmentioning

confidence: 99%

Contour-Aware Polyp Segmentation in Colonoscopy Images Using Detailed Upsampling Encoder-Decoder Networks

Nguyen

Lee

2020

IEEE Access

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Colorectal cancer has become one of the most common cause of cancer mortality worldwide, with a five-year survival rate of over 50%. Additionally, the potential of some common polyp types to progress to colorectal cancer is considered high. Colonoscopy is the most common method for finding and removing polyps. However, during colonoscopy, a significant number of polyps is missed as a result of human error mistakes. Thus, this study was primarily motivated by the need to obtain an early and accurate diagnosis of polyps detected in colonoscopy images. In this paper, we propose a new polyp segmentation method based on an architecture of multi-model deep encoder-decoder networks called MED-Net. Not only does this architecture obtain multi-level contextual information by extracting discriminative features at different effective fields-of-view and multiple image scales, it also can substantially do upsample more correctly to produce better prediction. It is also able to capture more accurate polyp boundaries by using multiscale effective decoders. Moreover, we also present a complementary strategy for improving the method's segmentation performance based on a combination of a boundary-aware data augmentation method and an effective weighted loss function. The purpose of this strategy is to allow our deep learning network to sequentially focus on poorly defined polyp boundaries, which are caused by the non-specular transition zone between the polyp and non-polyp regions. To provide a general view of the proposed method, our network was trained and evaluated on four well-known dataset CVC-ColonDB, CVC-ClinicDB, ASU-Mayo Clinic Colonoscopy Video Database, and ETIS-LaribPolypDB. Our results show that our MED-Net significantly outperforms state-of-the-art methods.

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Semi-Supervised Nests of Melanocytes Segmentation Method Using Convolutional Autoencoders

Cited by 28 publications

References 50 publications

Automatic Detection Method for Cancer Cell Nucleus Image Based on Deep-Learning Analysis and Color Layer Signature Analysis Algorithm

Automatic Detection Method for Cancer Cell Nucleus Image Based on Deep-Learning Analysis and Color Layer Signature Analysis Algorithm

Towards Accurate Diagnosis of Skin Lesions Using Feedforward Back Propagation Neural Networks

Contour-Aware Polyp Segmentation in Colonoscopy Images Using Detailed Upsampling Encoder-Decoder Networks

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