scholarly journals Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector

2018 ◽  
pp. 502-510 ◽  
Author(s):  
Sumedha Singla ◽  
Mingming Gong ◽  
Siamak Ravanbakhsh ◽  
Frank Sciurba ◽  
Barnabas Poczos ◽  
...  
Keyword(s):  
Image Patches ◽  
Discriminative Approach

scholarly journals Salience-based object prioritization during active viewing of naturalistic scenes in young and older adults

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Antje Nuthmann ◽  
Immo Schütz ◽  
Wolfgang Einhäuser
Keyword(s):  
Older Adults ◽  
Mixed Models ◽  
Age Groups ◽  
The Elderly ◽  
Unique Contribution ◽  
Visual Salience ◽  
Unit Of Analysis ◽  
Scientific Debate ◽  
Object Based ◽  
Image Patches

AbstractWhether fixation selection in real-world scenes is guided by image salience or by objects has been a matter of scientific debate. To contrast the two views, we compared effects of location-based and object-based visual salience in young and older (65 + years) adults. Generalized linear mixed models were used to assess the unique contribution of salience to fixation selection in scenes. When analysing fixation guidance without recurrence to objects, visual salience predicted whether image patches were fixated or not. This effect was reduced for the elderly, replicating an earlier finding. When using objects as the unit of analysis, we found that highly salient objects were more frequently selected for fixation than objects with low visual salience. Interestingly, this effect was larger for older adults. We also analysed where viewers fixate within objects, once they are selected. A preferred viewing location close to the centre of the object was found for both age groups. The results support the view that objects are important units of saccadic selection. Reconciling the salience view with the object view, we suggest that visual salience contributes to prioritization among objects. Moreover, the data point towards an increasing relevance of object-bound information with increasing age.

scholarly journals Deep bilinear features for Her2 scoring in digital pathology

2017 ◽  
Vol 3 (2) ◽  
pp. 811-814 ◽  
Author(s):  
Erik Rodner ◽  
Marcel Simon ◽  
Joachim Denzler
Keyword(s):  
Digital Pathology ◽  
Training Data ◽  
Large Set ◽  
Second Order Statistics ◽  
Over Expression ◽  
Point Of Interest ◽  
Winning Team ◽  
Image Patches ◽  
Parameterized Model ◽  
The University

AbstractWe present an automated approach for rating HER2 over-expressions in given whole-slide images of breast cancer histology slides. The slides have a very high resolution and only a small part of it is relevant for the rating.Our approach is based on Convolutional Neural Networks (CNN), which are directly modelling the whole computer vision pipeline, from feature extraction to classification, with a single parameterized model. CNN models have led to a significant breakthrough in a lot of vision applications and showed promising results for medical tasks. However, the required size of training data is still an issue. Our CNN models are pre-trained on a large set of datasets of non-medical images, which prevents over-fitting to the small annotated dataset available in our case. We assume the selection of the probe in the data with just a single mouse click defining a point of interest. This is reasonable especially for slices acquired together with another sample. We sample image patches around the point of interest and obtain bilinear features by passing them through a CNN and encoding the output of the last convolutional layer with its second-order statistics.Our approach ranked second in the Her2 contest held by the University of Warwick achieving 345 points compared to 348 points of the winning team. In addition to pure classification, our approach would also allow for localization of parts of the slice relevant for visual detection of Her2 over-expression.

scholarly journals Investigating the Impact of the Bit Depth of Fluorescence-Stained Images on the Performance of Deep Learning-Based Nuclei Instance Segmentation

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 967
Author(s):  
Amirreza Mahbod ◽  
Gerald Schaefer ◽  
Christine Löw ◽  
Georg Dorffner ◽  
Rupert Ecker ◽  
...  
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Computer Assisted ◽  
Important Criterion ◽  
Histological Image ◽  
Computer Mediated ◽  
Image Patches ◽  
Processing Techniques ◽  
The Impact ◽  
Instance Segmentation

Nuclei instance segmentation can be considered as a key point in the computer-mediated analysis of histological fluorescence-stained (FS) images. Many computer-assisted approaches have been proposed for this task, and among them, supervised deep learning (DL) methods deliver the best performances. An important criterion that can affect the DL-based nuclei instance segmentation performance of FS images is the utilised image bit depth, but to our knowledge, no study has been conducted so far to investigate this impact. In this work, we released a fully annotated FS histological image dataset of nuclei at different image magnifications and from five different mouse organs. Moreover, by different pre-processing techniques and using one of the state-of-the-art DL-based methods, we investigated the impact of image bit depth (i.e., eight bits vs. sixteen bits) on the nuclei instance segmentation performance. The results obtained from our dataset and another publicly available dataset showed very competitive nuclei instance segmentation performances for the models trained with 8 bit and 16 bit images. This suggested that processing 8 bit images is sufficient for nuclei instance segmentation of FS images in most cases. The dataset including the raw image patches, as well as the corresponding segmentation masks is publicly available in the published GitHub repository.

Visual Tracking via Subspace Learning: A Discriminative Approach

2017 ◽  
Vol 126 (5) ◽  
pp. 515-536 ◽  
Author(s):  
Yao Sui ◽  
Yafei Tang ◽  
Li Zhang ◽  
Guanghui Wang
Keyword(s):  
Visual Tracking ◽  
Subspace Learning ◽  
Discriminative Approach

scholarly journals Deep Learning for the Classification of Non-Hodgkin Lymphoma on Histopathological Images

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2419
Author(s):  
Georg Steinbuss ◽  
Mark Kriegsmann ◽  
Christiane Zgorzelski ◽  
Alexander Brobeil ◽  
Benjamin Goeppert ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hodgkin Lymphoma ◽  
Expert Knowledge ◽  
B Cell Lymphoma ◽  
High Accuracy ◽  
Lymphocytic Leukemia ◽  
Non Hodgkin Lymphoma ◽  
Image Patches ◽  
Histopathological Images

The diagnosis and the subtyping of non-Hodgkin lymphoma (NHL) are challenging and require expert knowledge, great experience, thorough morphological analysis, and often additional expensive immunohistological and molecular methods. As these requirements are not always available, supplemental methods supporting morphological-based decision making and potentially entity subtyping are required. Deep learning methods have been shown to classify histopathological images with high accuracy, but data on NHL subtyping are limited. After annotation of histopathological whole-slide images and image patch extraction, we trained and optimized an EfficientNet convolutional neuronal network algorithm on 84,139 image patches from 629 patients and evaluated its potential to classify tumor-free reference lymph nodes, nodal small lymphocytic lymphoma/chronic lymphocytic leukemia, and nodal diffuse large B-cell lymphoma. The optimized algorithm achieved an accuracy of 95.56% on an independent test set including 16,960 image patches from 125 patients after the application of quality controls. Automatic classification of NHL is possible with high accuracy using deep learning on histopathological images and routine diagnostic applications should be pursued.

Medical image registration using sparse coding of image patches

2016 ◽  
Vol 73 ◽  
pp. 56-70 ◽  
Author(s):  
Maryam Afzali ◽  
Aboozar Ghaffari ◽  
Emad Fatemizadeh ◽  
Hamid Soltanian-Zadeh
Keyword(s):  
Image Registration ◽  
Sparse Coding ◽  
Medical Image ◽  
Medical Image Registration ◽  
Image Patches

scholarly journals Quantifying peripheral and foveal perceived differences in natural image patches to predict visual search performance

2016 ◽  
Vol 16 (10) ◽  
pp. 18 ◽  
Author(s):  
Anna E. Hughes ◽  
Rosy V. Southwell ◽  
Iain D. Gilchrist ◽  
David J. Tolhurst
Keyword(s):  
Visual Search ◽  
Natural Image ◽  
Search Performance ◽  
Image Patches

Learning CNN to Pair UAV Video Image Patches

2017 ◽  
Vol 10 (12) ◽  
pp. 5752-5768 ◽  
Author(s):  
Yuting Chen ◽  
Lihua Liu ◽  
Zhiqiang Gong ◽  
Ping Zhong
Keyword(s):  
Video Image ◽  
Image Patches
Sign in / Sign up

Export Citation Format

Share Document