Researcher:
Cansız, Selahattin

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Selahattin

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Cansız

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Cansız, Selahattin

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20232

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Researcher Of Publications: search.filters.isAuthorOfPublication.3b0777bd-3620-445d-87a7-5f73fb19a4ac

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    Histopathological classification of colon tissue images with self-supervised models
    (IEEE, 2023) Department of Computer Engineering; Department of Computer Engineering; Erden, Mehmet Bahadır; Cansız, Selahattin; Demir, Çiğdem Gündüz; Graduate School of Sciences and Engineering; College of Engineering
    Deep learning techniques have demonstrated their ability to facilitate medical image diagnostics by offering more precise and accurate predictions. Convolutional neural network (CNN) architectures have been employed for a decade as the primary approach to enable automated diagnosis. On the other hand, recently proposed vision transformers (ViTs) based architectures have shown success in various computer vision tasks. However, their efficacy in medical image classification tasks remains largely unexplored due to their requirement for large datasets. Nevertheless, significant performance gains can be achieved by leveraging self-supervised learning techniques through pretraining. This paper analyzes performance of self-supervised pretrained networks in medical image classification tasks. Results on colon histopathology images revealed that CNN based architectures are more effective when trained from scratch, while pretrained models could achieve similar levels of performance with limited data.
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    FourierNet: shape-preserving network for Henle's fiber layer segmentation in optical coherence tomography images
    (Institute of Electrical and Electronics Engineers (IEEE), 2023) N/A; N/A; N/A; N/A; N/A; Department of Computer Engineering; Department of Computer Engineering; Cansız, Selahattin; Kesim, Cem; Bektaş, Şevval Nur; Kulalı, Zeynep Umut; Hasanreisoğlu, Murat; Demir, Çiğdem Gündüz; PhD Student; Teaching Faculty; Undergraduate Student; Undergraduate Student; Faculty Member; Faculty Member; Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); Graduate School of Sciences and Engineering; School of Medicine; School of Medicine; School of Medicine; School of Medicine; College of Engineering; N/A; 387367; N/A; N/A; 182001; 43402
    Henle's fiber layer (HFL), a retinal layer located in the outer retina between the outer nuclear and outer plexiform layers (ONL and OPL, respectively), is composed of uniformly linear photoreceptor axons and Muller cell processes. However, in the standard optical coherence tomography (OCT) imaging, this layer is usually included in the ONL since it is difficult to perceive HFL contours on OCT images. Due to its variable reflectivity under an imaging beam, delineating the HFL contours necessitates directional OCT, which requires additional imaging. This paper addresses this issue by introducing a shape-preserving network, FourierNet, which achieves HFL segmentation in standard OCT scans with the target performance obtained when directional OCT is available. FourierNet is a new cascaded network design that puts forward the idea of benefiting the shape prior of the HFL in the network training. This design proposes to represent the shape prior by extracting Fourier descriptors on the HFL contours and defining an additional regression task of learning these descriptors. FourierNet then formulates HFL segmentation as concurrent learning of regression and classification tasks, in which estimated Fourier descriptors are used together with the input image to construct the HFL segmentation map. Our experiments on 1470 images of 30 OCT scans of healthy-looking macula reveal that quantifying the HFL shape with Fourier descriptors and concurrently learning them with the main segmentation task leads to significantly better results. These findings indicate the effectiveness of designing a shape-preserving network to facilitate HFL segmentation without performing directional OCT imaging.