Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: An experimental study

Despotovic, Vladimir; Kim, Sang-Yoon; Hau, Ann-Christin; Kakoichankava, Aliaksandra; Klamminger, Gilbert Georg; Borgmann, Felix Bruno Kleine; Frauenknecht, Katrin B.M.; Mittelbronn, Michel; Nazarov, Petr V.

doi:10.1016/j.heliyon.2024.e27515

Heliyon

2024

DOI: 10.1016/j.heliyon.2024.e27515

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Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: An experimental study

Vladimir Despotovic,

Sang-Yoon Kim,

Ann-Christin Hau

et al.

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2024

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Cited by 3 publications

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A multi-class brain tumor grading system based on histopathological images using a hybrid YOLO and RESNET networks

Elazab,

Gab-Allah,

Elmogy

2024

Sci Rep

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Gliomas are primary brain tumors caused by glial cells. These cancers’ classification and grading are crucial for prognosis and treatment planning. Deep learning (DL) can potentially improve the digital pathology investigation of brain tumors. In this paper, we developed a technique for visualizing a predictive tumor grading model on histopathology pictures to help guide doctors by emphasizing characteristics and heterogeneity in forecasts. The proposed technique is a hybrid model based on YOLOv5 and ResNet50. The function of YOLOv5 is to localize and classify the tumor in large histopathological whole slide images (WSIs). The suggested technique incorporates ResNet into the feature extraction of the YOLOv5 framework, and the detection results show that our hybrid network is effective for identifying brain tumors from histopathological images. Next, we estimate the glioma grades using the extreme gradient boosting classifier. The high-dimensional characteristics and nonlinear interactions present in histopathology images are well-handled by this classifier. DL techniques have been used in previous computer-aided diagnosis systems for brain tumor diagnosis. However, by combining the YOLOv5 and ResNet50 architectures into a hybrid model specifically designed for accurate tumor localization and predictive grading within histopathological WSIs, our study presents a new approach that advances the field. By utilizing the advantages of both models, this creative integration goes beyond traditional techniques to produce improved tumor localization accuracy and thorough feature extraction. Additionally, our method ensures stable training dynamics and strong model performance by integrating ResNet50 into the YOLOv5 framework, addressing concerns about gradient explosion. The proposed technique is tested using the cancer genome atlas dataset. During the experiments, our model outperforms the other standard ways on the same dataset. Our results indicate that the proposed hybrid model substantially impacts tumor subtype discrimination between low-grade glioma (LGG) II and LGG III. With 97.2% of accuracy, 97.8% of precision, 98.6% of sensitivity, and the Dice similarity coefficient of 97%, the proposed model performs well in classifying four grades. These results outperform current approaches for identifying LGG from high-grade glioma and provide competitive performance in classifying four categories of glioma in the literature.

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A multi-class brain tumor grading system based on histopathological images using a hybrid YOLO and RESNET networks

Elazab,

Gab-Allah,

Elmogy

2024

Sci Rep

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Grade 4 astrocytoma vs. grade 4 glioblastoma: is there any clue in H&E?

Ersöz,

Berber,

Heper

2024

International Journal of Neuroscience

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Self-supervised learning framework application for medical image analysis: a review and summary

Zeng,

Abdullah,

Sumari

2024

BioMed Eng OnLine

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Manual annotation of medical image datasets is labor-intensive and prone to biases. Moreover, the rate at which image data accumulates significantly outpaces the speed of manual annotation, posing a challenge to the advancement of machine learning, particularly in the realm of supervised learning. Self-supervised learning is an emerging field that capitalizes on unlabeled data for training, thereby circumventing the need for extensive manual labeling. This learning paradigm generates synthetic pseudo-labels through pretext tasks, compelling the network to acquire image representations in a pseudo-supervised manner and subsequently fine-tuning with a limited set of annotated data to achieve enhanced performance. This review begins with an overview of prevalent types and advancements in self-supervised learning, followed by an exhaustive and systematic examination of methodologies within the medical imaging domain from 2018 to September 2024. The review encompasses a range of medical image modalities, including CT, MRI, X-ray, Histology, and Ultrasound. It addresses specific tasks, such as Classification, Localization, Segmentation, Reduction of False Positives, Improvement of Model Performance, and Enhancement of Image Quality. The analysis reveals a descending order in the volume of related studies, with CT and MRI leading the list, followed by X-ray, Histology, and Ultrasound. Except for CT and MRI, there is a greater prevalence of studies focusing on contrastive learning methods over generative learning approaches. The performance of MRI/Ultrasound classification and all image types segmentation still has room for further exploration. Generally, this review can provide conceptual guidance for medical professionals to combine self-supervised learning with their research.

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Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: An experimental study

Cited by 3 publications

References 47 publications

A multi-class brain tumor grading system based on histopathological images using a hybrid YOLO and RESNET networks

A multi-class brain tumor grading system based on histopathological images using a hybrid YOLO and RESNET networks

Grade 4 astrocytoma vs. grade 4 glioblastoma: is there any clue in H&E?

Self-supervised learning framework application for medical image analysis: a review and summary

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