28 papers · ranked by Valyu relevance
Narinder Singh Punn, Sonali Agarwal
With the advent of advancements in deep learning approaches, such as deep convolution neural network, residual neural network, adversarial network; U-Net architectures are most widely utilized in biomedical image segmentation to address the automation in identification and detection of the target regions or subregions.…
Xiaoyan Jiang, Zuojin Hu, Shuihua Wang, Yudong Zhang + 1 more
Simple Summary Deep learning has succeeded greatly in medical image-based cancer diagnosis. To help readers better understand the current research status and ideas, this article provides a detailed overview of the working mechanisms and use cases of commonly used radiological imaging and histopathology, the basic…
Yuanbo Wang, Unaiza Ahsan, Hanyan Li, Matthew Hagen
| Introduction 1.1 | Overview | . | . | . | | | | | | | | | | | | | | | | 113 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | | | | | | . | . | . | . | . | . | . | . | . | . | . | . | . | . | . | 113 | | 1.2 | Convolutional Neural…
Yan Xu, Rixiang Quan, Weiting Xu, Yi Huang + 3 more
Medical image segmentation plays a critical role in accurate diagnosis and treatment planning, enabling precise analysis across a wide range of clinical tasks. This review begins by offering a comprehensive overview of traditional segmentation techniques, including thresholding, edge-based methods, region-based…
İrem Ülkü, Erdem Akagündüz
Semantic segmentation is the pixel-wise labelling of an image. Since the problem is defined at the pixel level, determining image class labels only is not acceptable, but localising them at the original image pixel resolution is necessary. Boosted by the extraordinary ability of convolutional neural networks (CNN) in…
Jianglin Zhang, Yue Qiu, Li Peng, Qiuhong Zhou + 2 more
Background Diabetes mellitus (DM) is a chronic disease with hyperglycemia. If not treated in time, it may lead to lower limb amputation. At the initial stage, the detection of diabetes-related foot ulcer (DFU) is very difficult. Deep learning has demonstrated state-of-the-art performance in various fields and has been…
Amin Valizadeh, Morteza Shariatee
Image medical semantic segmentation has been employed in various areas, including medical imaging, computer vision, and intelligent transportation. In this study, the method of semantic segmenting images is split into two sections: the method of the deep neural network and previous traditional method. The traditional…
Mohammad D. Alahmadi, Bruno Märkl
Automatic medical image segmentation is an essential step toward accurate diseases diagnosis and designing a follow-up treatment. This assistive method facilitates the cancer detection process and provides a benchmark to highlight the affected area. The U-Net model has become the standard design choice. Although the…
Abubaker Abdelrahman, Serestina Viriri, Caroline Petitjean
Cure rates for kidney cancer vary according to stage and grade; hence, accurate diagnostic procedures for early detection and diagnosis are crucial. Some difficulties with manual segmentation have necessitated the use of deep learning models to assist clinicians in effectively recognizing and segmenting tumors. Deep…
M. Krithika alias AnbuDevi, K. Suganthi, Costin Teodor Streba
In biomedical image analysis, information about the location and appearance of tumors and lesions is indispensable to aid doctors in treating and identifying the severity of diseases. Therefore, it is essential to segment the tumors and lesions. MRI, CT, PET, ultrasound, and X-ray are the different imaging systems to…
Nicola K. Dinsdale, Mark Jenkinson, Ana I. L. Namburete
Acquisition of high quality manual annotations is vital for the development of segmentation algorithms. However, to create them we require a substantial amount of expert time and knowledge. Large numbers of labels are required to train convolutional neural networks due to the vast number of parameters that must be…
Rajdeep Das, Ajoy Mondal, Tapan Chakraborty, Kuntal Ghosh
Grain segmentation of sandstone that is partitioning the grain from its surrounding matrix/cement in the thin section is the primary step for computeraided mineral identification and sandstone classification. The microscopic images of sandstone contain many mineral grains and their surrounding matrix/cement. The…
Mehdi Seifi, Damian Dalle Nogare, Juan Battagliotti, Vera Galinova + 4 more
Once the work at the microscope is done, biological discoveries rely heavily on proper downstream analysis. This often amounts to first segmenting the biological objects of interest in the image before performing a quantitative analysis. Deep-learning (DL) is nowadays ubiquitous in such segmentation tasks. However, DL…
Matthew Amodio, Feng Gao, Arman Avesta, Sanjay Aneja + 3 more
'Lucian V. Del Priore' 'Jay Wang' 'Smita Krishnaswamy'] In this work we introduce CUTS (Contrastive and Unsupervised Training for Segmentation), a fully unsupervised deep learning framework for medical image segmentation to better utilize the vast majority of imaging data that is not labeled or annotated. Segmenting…
Abolfazl Zargari, Najmeh Mashhadi, S. Ali Shariati
The application of deep learning is rapidly transforming the field of bioimage analysis. While deep learning has shown great promise in complex microscopy tasks such as single-cell segmentation, the development of generalizable foundation deep learning segmentation models is hampered by the scarcity of large and…
Alex Matskevych, Adrian Wolny, Constantin Pape, Anna Kreshuk
The remarkable performance of Convolutional Neural Networks on image segmentation tasks comes at the cost of a large amount of pixelwise annotated images that have to be segmented for training. In contrast, feature-based learning methods, such as the Random Forest, require little training data, but never reach the…
Amirhossein Sajedi, Mohammad Javad Fadaeieslam
In view of the recent paradigm shift in deep AI-based image processing methods, medical image processing has advanced considerably. In this study, we propose a novel deep neural network (DNN), entitled InceptNet, in the scope of medical image processing, for early disease detection and segmentation of medical images in…
Bogdan Kochetov, Phoenix Bell, Paulo S. Garcia, Akram S. Shalaby + 12 more
Multiplexed imaging technologies have made it possible to interrogate complex tumor microenvironments at sub-cellular resolution within their native spatial context. However, proper quantification of this complexity requires the ability to easily and accurately segment cells into their sub-cellular compartments. Within…
Dario Sitnik, Ivica Kopriva
— Accurate segmentation of medical images is essential for diagnosis and treatment of diseases. These problems are solved by highly complex models, such as deep networks (DN), requiring a large amount of labeled data for training. Thereby, many DNs possess task- or imaging modality specific architectures with a…
Nils Körber
In recent years the amount of data generated by imaging techniques has grown rapidly along with increasing computational power and the development of deep learning algorithms. To address the need for powerful automated image analysis tools for a broad range of applications in the biomedical sciences, we present the…
Jessica Loke, Amber M. Brands, Natalie Cappaert, H. Steven Scholte
Visual processing in biological and artificial neural networks has been extensively studied through the lens of object recognition. While deep convolutional neural networks (DCNNs) have demonstrated hierarchical feature extraction similar to biological systems (3; 33), recent findings reveal a growing discrepancy…
Kohulan Rajan, Henning Otto Brinkhaus, M. Isabel Agea, Achim Zielesny + 1 more
The number of publications describing chemical structures has increased steadily over the last decades. However, the majority of published chemical information is currently not available in machine-readable form in public databases. It remains a challenge to automate the process of information extraction in a way that…
Henning Otto Brinkhaus, Kohulan Rajan, Achim Zielesny, Christoph Steinbeck
The development of deep learning-based optical chemical structure recognition (OCSR) systems has led to a need for datasets of chemical structure depictions. The diversity of the features in the training data is an important factor for the generation of deep learning systems that generalise well and are not overfit to…
Chloe A. Game, Nils Piechaud, Kerry L. Howell
Deep learning (DL) is a powerful tool to extract ecological information from large image datasets efficiently and consistently. However, applying these methods remains challenging, due in part to the complexity of DL workflows and the dynamic nature of available tools. To address this, we created a practical guide and…
Nathan Frey, Ryan Soklaski, Simon Axelrod, Siddharth Samsi + 3 more
Massive scale, both in terms of data availability and computation, enables significant breakthroughs in key application areas of deep learning such as natural language processing (NLP) and computer vision. There is emerging evidence that scale may be a key ingredient in scientific deep learning, but the importance of…
Authors not listed
The global drive towards net-zero has accelerated the adoption of carbon fibre reinforced polymers (CFRP) for lightweight structures in various sectors such as aerospace, automotive, energy and biomedical. Mechanical machining of CFRP is often necessary to meet dimensional or assembly-related requirements. However…
Kohulan Rajan, Henning Otto Brinkhaus, Achim Zielesny, Christoph Steinbeck
Accurate recognition of hand-drawn chemical structures is crucial for digitising hand-written chemical information found in traditional laboratory notebooks or for facilitating stylus-based structure entry on tablets or smartphones. However, the inherent variability in hand-drawn structures poses challenges for…
Derek van Tilborg, Helena Brinkmann, Emanuele Criscuolo, Luke Rossen + 2 more
Deep learning is becoming increasingly relevant in drug discovery, from de novo design to protein structure prediction and synthesis planning. However, it is often challenged by the small data regimes typical of certain drug discovery tasks. In such scenarios, deep learning approaches – which are notoriously…