We developed a weakly supervised deep learning algorithm, with an emphasis on a novel attention mechanism, to automatically classify RV strain on CTPA.
A fundamental step in the recovering of the 3D single-particle structure is to align its 2D projections; thus, the construction of a canonical representation with a fixed rotation angle is required.
The outbreak of COVID-19 has lead to a global effort to decelerate the pandemic spread.
In this work, we estimate the severity of pneumonia in COVID-19 patients and conduct a longitudinal study of disease progression.
In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues.
Content-based retrieval supports a radiologist decision making process by presenting the doctor the most similar cases from the database containing both historical diagnosis and further disease development history.
However, in a previous study, we have shown that binary auxiliary tasks are inferior to the usage of a rough similarity estimate that are derived from data annotations.
In this work, we study the combination of these two approaches for the problem of liver lesion segmentation and classification.
The outbreak of the novel coronavirus, officially declared a global pandemic, has a severe impact on our daily lives.
Using HU based segmentation of bone structures in the CT domain, a synthetic 2D "Bone x-ray" DRR is produced and used for training the network.
We conducted multiple retrospective experiments to analyze the performance of the system in the detection of suspected COVID-19 thoracic CT features and to evaluate evolution of the disease in each patient over time using a 3D volume review, generating a Corona score.
Magnetic resonance imaging (MRI) of thigh and calf muscles is one of the most effective techniques for estimating fat infiltration into muscular dystrophies.
The recent emergence of a large Chest X-ray dataset opened the possibility for learning features that are specific to the X-ray analysis task.
Detection and segmentation of MS lesions is a complex task largely due to the extreme unbalanced data, with very small number of lesion pixels that can be used for training.
6 code implementations • 13 Jan 2019 • Patrick Bilic, Patrick Ferdinand Christ, Grzegorz Chlebus, Hao Chen, Qi Dou, Chi-Wing Fu, Xiao Han, Pheng-Ann Heng, Jürgen Hesser, Samuel Kadoury, Tomasz Konopczynski, Miao Le, Chunming Li, Xiaomeng Li, Jana Lipkovà, John Lowengrub, Hans Meine, Jan Hendrik Moltz, Chris Pal, Marie Piraud, Xiaojuan Qi, Jin Qi, Markus Rempfler, Karsten Roth, Andrea Schenk, Anjany Sekuboyina, Eugene Vorontsov, Ping Zhou, Christian Hülsemeyer, Marcel Beetz, Florian Ettlinger, Felix Gruen, Georgios Kaissis, Fabian Lohöfer, Rickmer Braren, Julian Holch, Felix Hofmann, Wieland Sommer, Volker Heinemann, Colin Jacobs, Gabriel Efrain Humpire Mamani, Bram van Ginneken, Gabriel Chartrand, An Tang, Michal Drozdzal, Avi Ben-Cohen, Eyal Klang, Marianne M. Amitai, Eli Konen, Hayit Greenspan, Johan Moreau, Alexandre Hostettler, Luc Soler, Refael Vivanti, Adi Szeskin, Naama Lev-Cohain, Jacob Sosna, Leo Joskowicz, Bjoern H. Menze
The best liver segmentation algorithm achieved a Dice score of 0. 96(MICCAI) whereas for tumor segmentation the best algorithm evaluated at 0. 67(ISBI) and 0. 70(MICCAI).
Training data is the key component in designing algorithms for medical image analysis and in many cases it is the main bottleneck in achieving good results.
Being able to provide a "normal" counterpart to a medical image can provide useful side information for medical imaging tasks like lesion segmentation or classification validated by our experiments.
Two 2D FCNN architectures were trained to accomplish the task: The first performs 2D lung segmentation which is used for normalization of the lung area.
Accurate segmentation of anatomical structures in chest radiographs is essential for many computer-aided diagnosis tasks.
We present a decision concept which explicitly takes into account the input multi-view structure, where for each case there is a different subset of relevant views.
Then we present a novel scheme for liver lesion classification using CNN.
Quantitative evaluation was conducted using an existing lesion detection software, combining the synthesized PET as a false positive reduction layer for the detection of malignant lesions in the liver.
In this paper, we present a data augmentation method that generates synthetic medical images using Generative Adversarial Networks (GANs).
In this work we propose a method for anatomical data augmentation that is based on using slices of computed tomography (CT) examinations that are adjacent to labeled slices as another resource of labeled data for training the network.
Automatic detection of liver lesions in CT images poses a great challenge for researchers.