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    ConvNext Mixer-Based Encoder Decoder Method for Nuclei Segmentation in Histopathology Images
    (Wiley, 2024) Firat, Hueseyin; Uzen, Hueseyin; Hanbay, Davut; Sengur, Abdulkadir
    Histopathology, vital in diagnosing medical conditions, especially in cancer research, relies on analyzing histopathology images (HIs). Nuclei segmentation, a key task, involves precisely identifying cell nuclei boundaries. Manual segmentation by pathologists is time-consuming, prompting the need for robust automated methods. Challenges in segmentation arise from HI complexities, necessitating advanced techniques. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have transformed nuclei segmentation. This study emphasizes feature extraction, introducing the ConvNext Mixer-based Encoder-Decoder (CNM-ED) model. Unlike traditional CNN based models, the proposed CNM-ED model enables the extraction of spatial and long context features to address the inherent complexities of histopathology images. This method leverages a multi-path strategy using a traditional CNN architecture as well as different paths focused on obtaining customized long context features using the ConvNext Mixer block structure that combines ConvMixer and ConvNext blocks. The fusion of these diverse features in the final segmentation output enables improved accuracy and performance, surpassing existing state-of-the-art segmentation models. Moreover, our multi-level feature extraction strategy is more effective than models using self-attention mechanisms such as SwinUnet and TransUnet, which have been frequently used in recent years. Experimental studies were conducted using five different datasets (TNBC, MoNuSeg, CoNSeP, CPM17, and CryoNuSeg) to analyze the performance of the proposed CNM-ED model. Comparisons were made with various CNN based models in the literature using evaluation metrics such as accuracy, AJI, macro F1 score, macro intersection over union, macro precision, and macro recall. It was observed that the proposed CNM-ED model achieved highly successful results across all metrics. Through comparisons with state-art-of models from the literature, the proposed CNM-ED model stands out as a promising advancement in nuclei segmentation, addressing the intricacies of histopathological images. The model demonstrates enhanced diagnostic capabilities and holds the potential for significant progress in medical research.
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    A novel hybrid attention gate based on vision transformer for the detection of surface defects
    (Springer London Ltd, 2024) Uzen, Hueseyin; Turkoglu, Muammer; Ozturk, Dursun; Hanbay, Davut
    Many advanced models have been proposed for automatic surface defect inspection. Although CNN-based methods have achieved superior performance among these models, it is limited to extracting global semantic details due to the locality of the convolution operation. In addition, global semantic details can achieve high success for detecting surface defects. Recently, inspired by the success of Transformer, which has powerful abilities to model global semantic details with global self-attention mechanisms, some researchers have started to apply Transformer-based methods in many computer-vision challenges. However, as many researchers notice, transformers lose spatial details while extracting semantic features. To alleviate these problems, in this paper, a transformer-based Hybrid Attention Gate (HAG) model is proposed to extract both global semantic features and spatial features. The HAG model consists of Transformer (Trans), channel Squeeze-spatial Excitation (sSE), and merge process. The Trans model extracts global semantic features and the sSE extracts spatial features. The merge process which consists of different versions such as concat, add, max, and mul allows these two different models to be combined effectively. Finally, four versions based on HAG-Feature Fusion Network (HAG-FFN) were developed using the proposed HAG model for the detection of surface defects. The four different datasets were used to test the performance of the proposed HAG-FFN versions. In the experimental studies, the proposed model produced 83.83%, 79.34%, 76.53%, and 81.78% mIoU scores for MT, MVTec-Texture, DAGM, and AITEX datasets. These results show that the proposed HAGmax-FFN model provided better performance than the state-of-the-art models.