<i>AttentionBoost</i>: Learning What to Attend for Gland Segmentation in Histopathological Images by Boosting Fully Convolutional Networks

Güneşli, Gözde Nur; Sökmensüer, Cenk; Gündüz-Demir, Çiğdem

doi:10.1109/tmi.2020.3015198

Cited by 9 publications

(6 citation statements)

References 23 publications

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“…These tests attempted to quantify the generalization capability of the previously exposed algorithms. For example, the DNNs (U-Net, KG network, and R-CNN) segmented the images of the dataset NucleusSegData [ 67 ] without fine-tuning. Such an effect could be a limitation; however, it is a typical process in real-life applications.…”

Section: Resultsmentioning

confidence: 99%

“…This assumption generally holds [ 65 ], but it is not true for all datasets. For example, some datasets are RGB, and they contain most of their information in the green channel [ 66 ] or even in the blue channel [ 67 ]. Therefore, in the proposed analysis, it is implied that the starting point is a grayscale version of every dataset with as much information as possible.…”

Section: Methods and Datamentioning

confidence: 99%

“…The second dataset was introduced in [ 69 ] and then used in [ 67 ]. It is composed of 61 RGB images with a resolution variable of approximately

cancer cells/nuclei taken from the Huh7 and HepG2 regions.…”

Section: Methods and Datamentioning

confidence: 99%

“…The underlying concept is that the dataset is processed to extract the brightness of cells/nuclei, to obtain a grayscale image suitable for further processing. For example, Hoechst staining data were extracted from the dataset NucleusSegData [ 67 ], which was obtained using U.V. light, and the color of such images was blue.…”

Section: Proposed Methodsmentioning

confidence: 99%

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NeuronAlg: An Innovative Neuronal Computational Model for Immunofluorescence Image Segmentation

Giacopelli

Migliore

Tegolo

2023

Sensors

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Background: Image analysis applications in digital pathology include various methods for segmenting regions of interest. Their identification is one of the most complex steps and therefore of great interest for the study of robust methods that do not necessarily rely on a machine learning (ML) approach. Method: A fully automatic and optimized segmentation process for different datasets is a prerequisite for classifying and diagnosing indirect immunofluorescence (IIF) raw data. This study describes a deterministic computational neuroscience approach for identifying cells and nuclei. It is very different from the conventional neural network approaches but has an equivalent quantitative and qualitative performance, and it is also robust against adversative noise. The method is robust, based on formally correct functions, and does not suffer from having to be tuned on specific data sets. Results: This work demonstrates the robustness of the method against variability of parameters, such as image size, mode, and signal-to-noise ratio. We validated the method on three datasets (Neuroblastoma, NucleusSegData, and ISBI 2009 Dataset) using images annotated by independent medical doctors. Conclusions: The definition of deterministic and formally correct methods, from a functional and structural point of view, guarantees the achievement of optimized and functionally correct results. The excellent performance of our deterministic method (NeuronalAlg) in segmenting cells and nuclei from fluorescence images was measured with quantitative indicators and compared with those achieved by three published ML approaches.

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Section: Resultsmentioning

confidence: 99%

Section: Methods and Datamentioning

confidence: 99%

“…The second dataset was introduced in [ 69 ] and then used in [ 67 ]. It is composed of 61 RGB images with a resolution variable of approximately

cancer cells/nuclei taken from the Huh7 and HepG2 regions.…”

Section: Methods and Datamentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%

See 2 more Smart Citations

NeuronAlg: An Innovative Neuronal Computational Model for Immunofluorescence Image Segmentation

Giacopelli

Migliore

Tegolo

2023

Sensors

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“…Although the explainability of decision is improved, the interpretability of the features is still problematic. AttentionBoost network is proposed in [6] for gland instance segmentation in histopathological images. It is based on a multi-attention learning model that, based on adaptive boosting, adjusts loss of fully convolutional networks to adaptively learn what to attend at each stage.…”

Section: Related Work a Brief Overview Of Some Specialized Deep Neura...mentioning

confidence: 99%

LEFM-Nets: Learnable Explicit Feature Map Deep Networks for Segmentation of Histopathological Images of Frozen Sections

Sitnik¹,

Kopriva²

2022

Preprint

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Accurate segmentation of medical images is essential for diagnosis and treatment of diseases. These problems are solved by highly complex models, such as deep networks (DN), requiring a large amount of labeled data for training. Thereby, many DNs possess task-or imaging modality specific architectures with a decisionmaking process that is often hard to explain and interpret. Here, we propose a framework that embeds existing DNs into a low-dimensional subspace induced by the learnable explicit feature map (LEFM) layer. Compared to the existing DN, the framework adds one hyperparameter and only modestly increase the number of learnable parameters. The method is aimed at, but not limited to, segmentation of lowdimensional medical images, such as color histopathological images of stained frozen sections. Since features in the LEFM layer are polynomial functions of the original features, proposed LEFM-Nets contribute to the interpretability of network decisions. In this work, we combined LEFM with the known networks: DeepLabv3+, UNet, UNet++ and MA-net. New LEFM-Nets are applied to the segmentation of adenocarcinoma of a colon in a liver from images of hematoxylin and eosin (H&E) stained frozen sections. LEFM-Nets are also tested on nuclei segmentation from images of H&E stained frozen sections of ten human organs. On the first problem, LEFM-Nets achieved statistically significant performance improvement in terms of micro balanced accuracy and F 1 score than original networks. When averaged over ten runs, LEFM-MA-net achieved balanced accuracy of 89.36% ± 1.28% compared to 88.02% ± 1.22% by the MA-net. Corresponding results for F 1 score are 84.96% ± 1.14% and 82.75% ± 1.10%. On the second problem, LEFM-Nets achieved only better performance in comparison with the original networks. LEFM-MA-net achieved balanced accuracy of 89.41% ± 0.29% compared to 89.30% ± 0.44% by the original MA-net. Results for F 1 score are 85.35% ± 0.25% and 85.12% ± 0.51%. The source code is available at https://github.com/dsitnik/lefm

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Nuclei and glands instance segmentation in histology images: a narrative review

2022

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AttentionBoost: Learning What to Attend for Gland Segmentation in Histopathological Images by Boosting Fully Convolutional Networks

Cited by 9 publications

References 23 publications

NeuronAlg: An Innovative Neuronal Computational Model for Immunofluorescence Image Segmentation

NeuronAlg: An Innovative Neuronal Computational Model for Immunofluorescence Image Segmentation

LEFM-Nets: Learnable Explicit Feature Map Deep Networks for Segmentation of Histopathological Images of Frozen Sections

Nuclei and glands instance segmentation in histology images: a narrative review

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