Biomedical Imaging Workshops (ISBI Workshops), IEEE International Symposium
2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)
4-4 April 2020
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Biomedical Imaging Workshops (ISBI Workshops), 2020 IEEE 17th International Symposium on
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ISBI Workshops 2020 Technical Table of Contents
ISBI Workshops 2020 Table of Contents
Pneumothorax Segmentation with Effective Conditioned Post-Processing in Chest X-Ray
Publication Year: 2020,Page(s):1 - 4
Cited by: Papers (11)
The automatic detection of abnormal elements in chest Xrays (CXR), such as pneumothorax, is important and challenging problem. Screening for unexpected findings or any surveys in the complicated conditions are the common scenarios for the radiologists in their clinical workflow, where the automated solutions are required. The pneumothorax can be caused by a blunt chest injury, damage from underlyi...Show More
A GMM Based Algorithm To Generate Point-Cloud And Its Application To Neuroimaging
Publication Year: 2020,Page(s):1 - 4
Cited by: Papers (7)
Recent years have witnessed the emergence of 3D medical imaging techniques with the development of 3D sensors and technology. Due to the presence of noise in image acquisition, registration is a must, yet incur error which propagates to the subsequent analysis. An alternative way to analyze medical imaging is by understanding the 3D shapes represented in terms of point-cloud. Though in the medical...Show More
C-Algl Net: Pathological Images Generate Diagnostic Results
Zongkai Lian;Haiqiong Yang;Fan Wu;Mingxin Li;Shancheng Jiang
Publication Year: 2020,Page(s):1 - 4
The lack of a clear correspondence between feature of lesion areas and corresponding pathological characteristics and the scarcity of high-quality histopathological image sets pose a great challenge to the establishment of interpretable computer-aided diagnostic systems. Therefore, we propose a new deep learning-based model, named as C-ALGL model (CNN-AttendLSTM-GenerateLSTM), which is able to gen...Show More
Deepsharpen: Deep-Learning Based Sharpening Of 3D Reconstruction Map From Cryo-Electron Microscopy
Publication Year: 2020,Page(s):1 - 4
Cited by: Papers (2)
Cryo-electron microscopy (cryo-EM) has proven to be a promising tool for recovering the 3D structure of biological macromolecules. The cryo-EM map which is reconstructed from a large set of projection images, is then used for recovering the atomic model of the molecule. The accuracy of the fitted atomic model depends on the quality of the cryo-EM map. Due to current limitations during imaging or r...Show More
A Novel U-Like Network For The Segmentation Of Thoracic Organs
Publication Year: 2020,Page(s):1 - 4
Cited by: Papers (3)
Accurate segmentation of organs at risk (OARs) in computed tomography (CT) is an essential step in radiation therapy for thoracic cancer treatment. However, the manual segmentation of OARs is time-consuming and subject to inter-observer variation. In this paper, we propose a novel U-like deep convolutional neural network (CNN) architecture, which adopts the encoder-decoder design, to automatically...Show More
Systematic Analysis And Automated Search Of Hyper-Parameters For Cell Classifier Training
Philipp Gräbel;Gregor Nickel;Martina Crysandt;Reinhild Herwartz;Melanie Baumann;Barbara M. Klinkhammer;Peter Boor;Tim H. Brümmendorf;Dorit Merhof
Publication Year: 2020,Page(s):1 - 4
Cited by: Papers (3)
Performance and robustness of neural networks depend on a suitable choice of hyper-parameters, which is important in research as well as for the final deployment of deep learning algorithms. While a manual systematical analysis can be too time consuming, a fully automatic search is very dependent on the kind of hyper-parameters. For a cell classification network, we assess the individual effects o...Show More
Joint Low Dose CT Denoising And Kidney Segmentation
Publication Year: 2020,Page(s):1 - 4
Cited by: Papers (3)
In this research, both image denoising and kidney segmentation tasks are addressed jointly via one multitask deep convolutional network. This multitasking scheme yields better results for both tasks compared to separate single-task methods. Also, to the best of our knowledge, this is a first time attempt at addressing these joint tasks in low-dose CT scans (LDCT). This new network is a conditional...Show More
Lung Lobe Segmentation With Automated Quality Assurance Using Deep Convolutional Neural Networks
Sundaresh Ram;Stephen M. Humphries;David A. Lynch;Craig J. Galban;Charles R. Hatt
Publication Year: 2020,Page(s):1 - 4
Cited by: Papers (1)
Despite good performance for medical image segmentation, deep convolutional neural networks (CNNs) have not been widely accepted in clinical practice as they are complex and tend to fail silently. Additionally, uncertainty in their predictions are not well understood, making them obscure and challenging to interpret. Automatically detecting possible failures in network predictions is important, as...Show More
Low-Dose Pet Image Restoration With 2D And 3D Network Prior Learning
Yu Gong;Hongming Shan;Yueyang Teng;Hairong Zheng;Ge Wang;Shanshan Wang
Publication Year: 2020,Page(s):1 - 4
Cited by: Papers (1)
Reducing the dose of positron emission tomography (PET) imaging is a hot research area for avoiding too much radiation exposure. However, low-dose imaging faces the challenges of different degradation factors such as noise and artifacts. To restore high-quality PET images, we propose a mixed 2D and 3D encoder-decoder network to draw the mapping prior between low-dose and normal-dose PET images und...Show More
Deeply-Supervised Multi-Dose Prior Learning For Low-Dose Pet Imaging
Yu Gong;Hongming Shan;Yueyang Teng;Hairong Zheng;Ge Wang;Shanshan Wang
Publication Year: 2020,Page(s):1 - 4
Cited by: Papers (1)
Positron emission tomography (PET) is an advanced imaging modality for tumor staging and therapy response. However, PET radiation exposure has raised public concerns and it is in need to develop low-dose PET imaging techniques. This paper proposes to explore prior information inherited in different levels of low-dose PET images with deep learning and then utilize them to estimate high-quality PET ...Show More
A Decision Support System For Retinal Image Defect Detection
Aparna Kanakatte;Jayavardhana Gubbi;Avik Ghose;Balamuralidhar Purushothaman
Publication Year: 2020,Page(s):1 - 4
Cited by: Papers (5)
Deep learning has become the de facto method for image classification. In this work, a common framework for decision support system is presented that can be reused for diagnosing multiple retinal clinical conditions. Retinal fundus images provide a non-invasive way to diagnose eye-related diseases like glaucoma and diabetic retinopathy (DR). State-of-the-art deep learning methods focus on the dete...Show More
Topological Signal Processing in Neuroimaging Studies
Yuan Wang;Roozbeh Behroozmand;Lorelei Phillip Johnson;Leonardo Bonilha;Julius Fridriksson
Publication Year: 2020,Page(s):1 - 4
Cited by: Papers (5)
Electroencephalography (EEG) is an important neuroimaging tool for understanding network disorders caused by neuroanatomical malformation or damage such as epilepsy and post-stroke aphasia. Topological data analysis (TDA) can decode patterns in EEG signals that are not captured by standard temporal and spectral features but at the same time reveal important information on the underlying brain proc...Show More
Sparse Anett For Solving Inverse Problems With Deep Learning
Publication Year: 2020,Page(s):1 - 4
Cited by: Papers (9)
We propose a sparse reconstruction framework (aNETT) for solving inverse problems. Opposed to existing sparse reconstruction techniques that are based on linear sparsifying transforms, we train an autoencoder network D○E with E acting as a nonlinear sparsifying transform and minimize a Tikhonov functional with learned regularizer formed by the ℓq-norm of the encoder coefficients and a penalty for ...Show More
Preliminary Studies On Training And Fine-Tuning Deep Denoiser Neural Networks In Learned D-Amp For Undersampled Real Mr Measurements
Publication Year: 2020,Page(s):1 - 4
Recently, deep learning based MR image reconstructions have shown outstanding performance. While there have been many direct mapping based methods by deep neural networks without taking advantage of known physical model of medical imaging modality, some groups investigated combining conventional model-based image reconstruction (MBIR) and learning based method to enhance performance and computatio...Show More
Optical coherence tomography (OCT) is a powerful tool for diagnosing many ophthalmic diseases that causes variations to the structure of the eyes. The size of edema and thickness of choroid layers can be ascertained by proper segmentation of OCT images of retina. This paper proposes a model using Convolutional Neural Network (CNN) for segmenting edema and choroid layers in OCT images. Our CNN mode...Show More
Csrgan: Medical Image Super-Resolution Using A Generative Adversarial Network
Publication Year: 2020,Page(s):1 - 4
Cited by: Papers (15)
Super-resolution medical image is vital for doctor’s diagnosis and quantitative analysis. In this work we propose a novel super-resolution generative adversarial network which combine conditional GAN (CGAN) and SRGAN, refer to it as CSRGAN to generate super-resolution (SR) images. We use differential geometric information including Jacobian determinant (JD) and curl vector (CV) as conditional inpu...Show More
Deep Quantized Representation For Enhanced Reconstruction
Akash Gupta;Abhishek Aich;Kevin Rodriguez;G. Venugopala Reddy;Amit K. Roy-Chowdhury
Publication Year: 2020,Page(s):1 - 4
Cited by: Papers (3)
While machine learning approaches have shown remarkable performance in biomedical image analysis, most of these methods rely on high-quality and accurate imaging data. However, collecting such data requires intensive and careful manual effort. One of the major challenges in imaging the Shoot Apical Meristem (SAM) of Arabidopsis thaliana, is that the deeper slices in the z-stack suffer from differe...Show More
Material Decomposition Problem in Spectral CT: A Transfer Deep Learning Approach
J Abascal;N Ducros;V Pronina;S Bussod;A Hauptmannz;S Arridge;P Douek;F Peyrin
Publication Year: 2020,Page(s):1 - 4
Cited by: Papers (2)
Current model-based variational methods used for solving the nonlinear material decomposition problem in spectral computed tomography rely on prior knowledge of the scanner energy response, but this is generally unknown or spatially varying. We propose a twostep deep transfer learning approach that can learn the energy response of the scanner and its variation across the detector pixels. First, we...Show More
Pixel-Based Iris and Pupil Segmentation in Cataract Surgery Videos Using Mask R-CNN
Natalia Sokolova;Mario Taschwer;Stephanie Sarny;Doris Putzgruber-Adamitsch;Klaus Schoeffmann
Publication Year: 2020,Page(s):1 - 4
Cited by: Papers (7)
Automatically detecting clinically relevant events in surgery video recordings is becoming increasingly important for documentary, educational, and scientific purposes in the medical domain. From a medical image analysis perspective, such events need to be treated individually and associated with specific visible objects or regions. In the field of cataract surgery (lens replacement in the human e...Show More
Detection Of Foreign Objects In Chest Radiographs Using Deep Learning
Hrishikesh Deshpande;Tim Harder;Axel Saalbach;Abhivyakti Sawarkar;Thomas Buelow
Publication Year: 2020,Page(s):1 - 4
Cited by: Papers (3)
We propose a deep learning framework for the automated detection of foreign objects in chest radiographs. Foreign objects can affect the diagnostic quality of an image and could affect the performance of CAD systems. Their automated detection could alert the technologists to take corrective actions. In addition, the detection of foreign objects such as pacemakers or placed devices could also help ...Show More
Scan-Specific Accelerated Mri Reconstruction Using Recurrent Neural Networks In A Regularized Self-Consistent Framework
SeyedAmir Hossein Hosseini;Burhaneddin Yaman;Chi Zhang;Kâmil Uğurbil;Steen Moeller;Mehmet Akçakaya
Publication Year: 2020,Page(s):1 - 4
Long scan duration remains a challenge for high-resolution MRI. Several accelerated imaging strategies have been proposed based on deep learning (DL) that require databases of fully-sampled images for training. However, scan-specific training is desired where individual variability is important, e.g. in free-breathing cardiac MRI, or where such datasets are not available due to scan time constrain...Show More
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The proceedings of this conference will be available for purchase through Curran Associates.
Biomedical Imaging Workshops (ISBI Workshops), 2020 IEEE 17th International Symposium on
- Print on DemandPurchase at Partner
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