Browsing by Subject "Object detection"

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    Automated mitosis detection in stained histopathological images using Faster R-CNN and stain techniques
    (De Gruyter, 2025-06-11) García-Salmerón, Jesús; García, José Manuel; Bernabé, Gregorio; González Férez, Pilar; Ingeniería y Tecnología de Computadores
    Accurate mitosis detection is essential for cancer diagnosis and treatment. Traditional manual counting by pathologists is time-consuming and may cause errors. This research investigates automated mitosis detection in stained histopathological images using Deep Learning (DL) techniques, particularly object detection models. We propose a two-stage object detection model based on Faster R-CNN to effectively detect mitosis within histopathological images. The stain augmentation and normalization techniques are also applied to address the significant challenge of domain shift in histopathological image analysis. The experiments are conducted using the MIDOG++ dataset, the most recent dataset from the MIDOG challenge. This research builds on our previous work, in which two one-stage frameworks, in particular on RetinaNet using fastai and PyTorch, are proposed. Our results indicate favorable F1-scores across various scenarios and tumor types, demonstrating the effectiveness of the object detection models. In addition, Faster R-CNN with stain techniques provides the most accurate and reliable mitosis detection, while RetinaNet models exhibit faster performance. Our results highlight the importance of handling domain shifts and the number of mitotic figures for robust diagnostic tools.
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    Validating retinaNet for the object detection-based mitosis detection in the MIDOG challenge
    (Springer Nature, 2025-04-25) García-Salmerón, Jesús; García, José Manuel; Bernabé García, Gregorio; González Férez, Pilar; Ingeniería y Tecnología de Computadores
    Mitosis detection is critical in histopathology for accurate diagnosis and prognosis of tumors, a topic of particular interest underscored by recent challenges. In this study, we focus on developing deep learning (DL) solutions to confront this challenge within the framework of the MIDOG challenge. Leveraging the newest MIDOG challenge dataset, the MIDOG++ dataset, we explore the efficacy of object detection models. Specifically, the RetinaNet model using fastai and PyTorch frameworks. We replicate and validate the reference work, RetinaNet, using fastai, and we propose the RetinaNet model using PyTorch. Through rigorous training and evaluation, we analyze the performance of these models in detecting mitotic figures, crucial for automating histopathological analysis and improving diagnostic accuracy. Our study demonstrates the effectiveness of the RetinaNet model in mitosis detection within histopathological images. Obtaining favorable F1 scores across the different scenarios and analyzing the relationship between different tumor types.