27 papers · ranked by Valyu relevance
Alberto García-García, Sergio Orts‐Escolano, Sergiu Oprea, Víctor Villena-Martínez + 1 more
'Víctor Villena-Martínez' 'José García‐Rodríguez'] Abstract—Image semantic segmentation is more and more being of interest for computer vision and machine learning researchers. Many applications on the rise need accurate and efficient segmentation mechanisms: autonomous driving, indoor navigation, and even virtual or…
Zeynettin Akkus, Alfiia Galimzianova, Assaf Hoogi, Daniel L. Rubin + 1 more
'Bradley J. Erickson'] Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over…
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…
Shervin Minaee, Yuri Boykov, Fatih Porikli, Antonio Plaza + 2 more
'Nasser Kehtarnavaz' 'Demetri Terzopoulos'] Abstract—Image segmentation is a key topic in image processing and computer vision with applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among many others. Various algorithms…
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…
Swarnendu Ghosh, Nibaran Das, Ishita Das, Ujjwal Maulik
The machine learning community has been overwhelmed by a plethora of deep learning based approaches. Many challenging computer vision tasks such as detection, localization, recognition and segmentation of objects in unconstrained environment are being efficiently addressed by various types of deep neural networks like…
Xi Liu, Kai-Wen Li, Ruijie Yang, Li-Sheng Geng
Lung cancer is the leading cause of cancer-related mortality for males and females. Radiation therapy (RT) is one of the primary treatment modalities for lung cancer. While delivering the prescribed dose to tumor targets, it is essential to spare the tissues near the targets-the so-called organs-at-risk (OARs). An…
Travers Ching, Daniel S. Himmelstein, Brett K. Beaulieu-Jones, Alexandr A. Kalinin + 23 more
Deep learning, which describes a class of machine learning algorithms, has recently showed impressive results across a variety of domains. Biology and medicine are data rich, but the data are complex and often ill-understood. Problems of this nature may be particularly well-suited to deep learning techniques. We…
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…
Yuanbo Wang, Unaiza Ahsan, Hanyan Li, Matthew Hagen
| Introduction 1.1 | Overview | . | . | . | | | | | | | | | | | | | | | | 113 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | | | | | | . | . | . | . | . | . | . | . | . | . | . | . | . | . | . | 113 | | 1.2 | Convolutional Neural…
Saeid Asgari Taghanaki, Kumar Abhishek, Joseph Cohen, Julien Cohen‐Adad + 1 more
'Julien Cohen‐Adad' 'Ghassan Hamarneh'] Abstract The semantic image segmentation task consists of classifying each pixel of an image into an instance, where each instance corresponds to a class. This task is a part of the concept of scene understanding or better explaining the global context of an image. In the medical…
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…
Ilya Belevich, Eija Jokitalo
Deep learning approaches are highly sought after solutions for coping with large amounts of collected datasets and are expected to become an essential part of imaging workflows. However, in most cases, deep learning is still considered as a complex task that only image analysis experts can master. DeepMIB addresses…
Clément Douarre, Richard Schielein, Carole Frindel, Stefan Gerth + 1 more
One of the most challenging computer vision problem in plant sciences is the segmentation of root and soil from X-ray tomography. So far, this has been addressed from classical image analysis methods. In this paper, we address this root/soil segmentation problem from X-ray tomography using a new deep learning…
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…
Erik Meijering
Deep learning of artificial neural networks has become the de facto standard approach to solving data analysis problems in virtually all fields of science and engineering. Also in biology and medicine, deep learning technologies are fundamentally transforming how we acquire, process, analyze, and interpret data, with…
Kaushik Dutta
Deep Learning networks have established themselves as providing state of art performance for semantic segmentation. These techniques are widely applied specifically to medical detection, segmentation and classification. The advent of the U-Net based architecture has become particularly popular for this application. In…
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…
N Seijdel, N Tsakmakidis, EHF De Haan, SM Bohte + 1 more
Feedforward deep convolutional neural networks (DCNNs) are, under specific conditions, matching and even surpassing human performance in object recognition in natural scenes. This performance suggests that the analysis of a loose collection of image features could support the recognition of natural object categories…
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…
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…
Hang Hu, Jyothsna Padmakumar Bindu, Julia Laskin
Mass spectrometry imaging (MSI) is widely used for the label-free molecular mapping of biological samples. The identification of co-localized molecules in MSI data is crucial to the understanding of biochemical pathways. However, complex MSI data are too large for manual annotation but too small for training deep…
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…
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…
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…
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Olfaction arises from the interaction of odorants with olfactory receptors, a process shaped by molecular geometry, electron distribution, and conformational preference. We present ConfDENSE, a Set2Set enhanced PointNet model that learns directly from Hirshfeld promolecule electron-density point clouds, preserving full…