DAN-NucNet++: A Bottom-Up Framework Using Dual Attention Network for Simultaneous Nuclei Segmentation and Classification in Digital Histology Images

Ahmad, Ibtihaj; Xia, Yong; Cui, Hengfei; Israr, Syed Muhammad

doi:10.2139/ssrn.4139007

Cited by 2 publications

(2 citation statements)

References 34 publications

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“…Third, in this work we focused on the accuracy of the semantic segmentation provided by sixteen architectures derived from the original UNet. Further work could consider the segmentation and classification, not of tissue regions, but of specific cells (e.g., Tumor Infiltrating Lymphocytes [77], [78] ), or nuclei (e.g., [79], [80], [81], [82] ), which can provide further information to analyze cancer datasets. Further, by using the nuclei as a starting point, it is possible then determine the extent of cell and from there isolate and analyze other structures like extracellular matrix [83].…”

Section: Discussionmentioning

confidence: 99%

DRD-UNet, a UNet-Like Architecture for Multi-Class Breast Cancer Semantic Segmentation

Ortega-Ruíz,

Karabağ,

Roman-Rangel

et al. 2024

IEEE Access

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Staining of histological slides with Hematoxylin and Eosin is widely used in clinical and laboratory settings as these dyes reveal nuclear structures as well as cytoplasm and collagen. For cancer diagnosis, these slides are used to recognize tissues and morphological changes. Tissue semantic segmentation is therefore important and at the same time a challenging and time-consuming task. This paper describes a UNet-like deep learning architecture called DRD-UNet, which adds a novel processing block called DRD (Dilation, Residual, and Dense block) to a UNet architecture. DRD is formed by the combination of dilated convolutions (D), residual connections (R), and dense layers (D). DRD-UNet was applied to the multi-class (tumor, stroma, inflammatory, necrosis, and other) semantic segmentation of histological images from breast cancer samples stained with Hematoxylin and Eosin. The histological images were released through the Breast Cancer Semantic Segmentation (BCSS) Challenge. DRD-UNet outperformed the original UNet architecture and 15 other UNet-based architectures on the segmentation of 12, 930 image patches extracted from regions of interest that ranged in size between 1036 × 1222 to 6813 × 7360 pixels. DRD-UNet obtained the best performance as measured with Jaccard similarity index, Dice coefficient, in a per-class comparison and accuracy for overall segmentation.

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Section: Discussionmentioning

confidence: 99%

DRD-UNet, a UNet-Like Architecture for Multi-Class Breast Cancer Semantic Segmentation

Ortega-Ruíz,

Karabağ,

Roman-Rangel

et al. 2024

IEEE Access

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“…As well as Deep learning (DL) architectures like Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) have attained exciting outcomes when compared to human performance in numerous tasks. CNNs are normally employed for automatic data removal from image data [11]. Numerous CNN frameworks are projected that manage such issues in unique methods and are employed affording to the necessity [12].…”

Section: Introductionmentioning

confidence: 99%

Group Teaching Optimization With Deep Learning-Driven Osteosarcoma Detection Using Histopathological Images

Alsubai,

Dutta,

Alghayadh

et al. 2024

IEEE Access

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Osteosarcoma is the most normal kind of cancer that arises in bones, which appears on the surface to resemble earlier types of bone cells that assist in forging new bone tissues, but the tissue in osteosarcoma is weaker and softer than normal bone tissue. The usage of automated techniques for the detection of osteosarcoma has the potential to mitigate the obligations and burdens confronted by pathologists owing to its abundant quantity of cases. Artificial intelligence (AI) has an emerging progress in diagnostic pathology. In recent years, numerous studies using deep learning (DL) techniques to histopathological images (HI) have been published. While several studies claim higher accuracy, they might lack generalization and fall into the pitfall of overfitting owing to the wide range of HI. The study objective is to enhance the diagnosis and detection of osteosarcoma by employing computer-assisted detection (CAD) and diagnoses (CADx). Technique like convolutional neural networks (CNN) make better prognoses for patient conditions and considerably reduce the surgeon's workload. CNN needs to be trained on the massive quantity of data to accomplish a remarkable performance. Therefore, the study presents a novel Group Teaching Optimization Algorithm with Deep Learning-Driven Osteosarcoma Detection on Histopathological Images (GTOADL-ODHI) technique. The purpose of the GTOADL-ODHI technique is to examine the HIs for the detection and classification of osteosarcoma. To accomplish this, the GTOADL-ODHI algorithm applies the Gaussian filtering (GF) method for image pre-processed to become rid of the noise. Besides, the capsule network (CapsNet) model is utilized for the extractor of the feature vector. Furthermore, the hyperparameter selection of the CapsNet approach takes place using the GTOA. Finally, the self-attention bidirectional long short-term memory (SA-BiLSTM) model can be employed for osteosarcoma recognition and classification. The widespread experimental analysis of the GTOADL-ODHI method is tested on the benchmark datasets. The simulation validation reported the optimum solution of the GTOADL-ODHI algorithm related to existing systems concerning distinct aspects.

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DAN-NucNet++: A Bottom-Up Framework Using Dual Attention Network for Simultaneous Nuclei Segmentation and Classification in Digital Histology Images

Cited by 2 publications

References 34 publications

DRD-UNet, a UNet-Like Architecture for Multi-Class Breast Cancer Semantic Segmentation

DRD-UNet, a UNet-Like Architecture for Multi-Class Breast Cancer Semantic Segmentation

Group Teaching Optimization With Deep Learning-Driven Osteosarcoma Detection Using Histopathological Images

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