Visualizing the Importance of Floor-Plan Image Features in Rent-Prediction Models

2020 ◽  
Author(s):  
Ryosuke Hattori ◽  
Kazushi Okamoto ◽  
Atsushi Shibata
Keyword(s):  
Prediction Models ◽  
Image Features ◽  
Floor Plan

Image Features for Automated Colorectal Polyp Classification Based on Clinical Prediction Models

2019 ◽  
Author(s):  
Michelle C. A. van Grinsven ◽  
Thom Scheeve ◽  
Ramon-Michel Schreuder ◽  
Fons van der Sommen ◽  
Erik J. Schoon ◽  
...  
Keyword(s):  
Prediction Models ◽  
Colorectal Polyp ◽  
Image Features ◽  
Clinical Prediction ◽  
Clinical Prediction Models

scholarly journals Convolutional Neural Networks in Image Retrieval System

2020 ◽  
Vol 9 (5) ◽  
pp. 123-129
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Image Retrieval ◽  
Convolutional Neural Networks ◽  
Medical Image ◽  
Retrieval System ◽  
Prediction Models ◽  
Image Features ◽  
Medical Image Retrieval ◽  
Image Retrieval System

Image is an important medium for monitoring the treatment responses of patient’s diseases by the physicians. There could be a tough task to organize and retrieve images in structured manner with respect to incredible increase of images in Hospitals. Text based image retrieval may prone to human error and may have large deviation across different images. Content-Based Medical Image Retrieval(CBMIR) system plays a major role to retrieve the required images from the huge database.Recent advances in Deep Learning (DL) have made greater achievements for solving complex problems in computer vision ,graphics and image processing. The deep architecture of Convolutional Neural Networks (CNN) can combine the low-level features into high-level features which could learn the semantic representation from images. Deep learning can help to extract, select and classify image features, measure the predictive target and gives prediction models to assist physician efficiently. The motivation of this paper is to provide the analysis of medical image retrieval system using CNN algorithm.

scholarly journals Developing global image feature analysis models to predict cancer risk and prognosis

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Bin Zheng ◽  
Yuchen Qiu ◽  
Faranak Aghaei ◽  
Seyedehnafiseh Mirniaharikandehei ◽  
Morteza Heidari ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cancer Risk ◽  
Prediction Models ◽  
Image Features ◽  
Image Feature ◽  
Learning Models ◽  
Study Results ◽  
Computer Aided ◽  
Predict Cancer Risk ◽  
Machine Learning Models

AbstractIn order to develop precision or personalized medicine, identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently. Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images, which include steps in detecting and segmenting suspicious regions or tumors, followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors. However, due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors, segmenting subtle regions is often difficult and unreliable. Additionally, ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches. In our recent studies, we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis. We trained and tested several models using images obtained from full-field digital mammography, magnetic resonance imaging, and computed tomography of breast, lung, and ovarian cancers. Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice. Furthermore, the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis. Therefore, the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.

Clinical parameters combined with radiomics features to predict the efficacy of immunotherapy for advanced non-small cell lung cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e21187-e21187
Author(s):  
Qian Zhao ◽  
Linlin Wang
Keyword(s):  
Predictive Models ◽  
Predictive Value ◽  
Mixed Model ◽  
Proportional Hazards ◽  
Prediction Models ◽  
Image Features ◽  
Cox Proportional Hazards ◽  
Lasso Regression ◽  
Discriminative Ability ◽  
3D Slicer

e21187 Background: Many tools have been developed to predict the efficacy of immunotherapy, such as LIPI, EPSILoN and mLIPI scores. The aim of this study was to determine the ability of to predict outcomes in Chinese aNSCLC patients treated with ICIs.With the development of imaging technology, radiomics has increasingly received a great deal of attention. We use 3D-slicer to delineat the ROI in the patient's CT image, extract a large number of features, and further screen out the radiomics features that have predictive value for the outcome. Methods: We retrospectively enrolled 317 patients with histologically proven aNSCLC (IIIB–IV) treated with ICIs. The discriminative ability of the predictive models was evaluated by AUC in the ROC analysis. Patients were randomly divided into training and validation cohorts using a 2：1 ratio. We used the semi-automatic segmentation method of the 3D-slicer platform to delineate the ROI of the tumor’s lesion area and extract 854 image features for each patient. In the training set of patients, the LASSO algorithm was used to screen the radiomic features.Hosmer-Lemeshow tests (H-L tests) were conducted to determine the fit of the prediction models. PFS and OS curves were generated using the Kaplan-Meier method and differences were assessed using log-rank tests. Univariate and multivariate analyses were performed using Cox proportional-hazards regression models. The glmnet R package was used for the LASSO regression method. The rms, Hmisc R packages used for C-index. Results: Among the 317 patients included in the study, the median OS and PFS were 14.2 months and 5.6 months, respectively and the ORR was 23.1%. The AUC values of LIPI, mLIPI, and EPSILoN scores for predicting PFS were 0.649 (95% CI: 0.588–0.709), 0.765 (95% CI: 0.713–0.818]), and 0.637 (95% CI: 0.567–0.698), respectively (P < 0.001 for all models). The AUC value of mLIPI scores was significantly higher than that of LIPI and EPSILoN scores (P < 0.05). The C-index of mLIPI was 0.645 (95% CI: 0.617-0.673). In this study, 5 radiomics features with predictive value were selected from radiomics features. The C-indexs of the radscore were 0.643 (95% CI: 0.602–0.684) and 0.632 (95% CI: 0.571–0.693). Then we combined mLIPI and radscore to obtain a mixed model. The C-index of the combination mLIPI scores with radscore for predicting PFS was 0.810 (95% CI: 0.770–0.849) and 0.706（CI: 0.633–0.778) in the training and validation cohorts, respectively. Conclusions: By externally validating LIPI, mLIPI, and EPSILoN scores, we found that all three of these predictive models could identify different prognostic subsets of patients treated with ICIs to statistically significant degrees. We also found that mLIPI had the highest accuracy among the three models. With the addition of radiomics features, the prediction performance of the mixed model has been further improved.

scholarly journals Automated classification of dry eye type analyzing interference fringe color images of tear film using machine learning techniques

2019 ◽  
Vol 2 (3) ◽  
pp. 28-35
Author(s):  
Katsumi Yabusaki ◽  
Reiko Arita ◽  
Takanori Yamauchi
Keyword(s):  
Dry Eye ◽  
Interference Fringe ◽  
Prediction Models ◽  
Tear Film ◽  
Image Features ◽  
Age Related Macular Degeneration ◽  
Machine Learning Techniques ◽  
Interference Fringes ◽  
Age Related ◽  
Type Classification

The unstable balance in secretions of lipids and aqueous fluid to tear film is a significant cause of dry eye disease (DED). Arita et al. demonstrated a simple but very effective method that classifies dry eye types to the aqueous deficient dry eye (ADDE) and the evaporative dry eye (EDE) by focusing on the dry eye type-unique appearances of interference fringe colors and patterns of tear films. We thought this simple classification is very helpful for diagnoses and treatments. However, diagnostic bias by unskilled observers remains an issue to be solved. The artificial intelligence (AI)-based support for diagnosis is one of the hottest topics in the field of ophthalmology research. We expected that the AI-based model would reduce bias in DED-type diagnoses. Many studies have been reported targeting retinal diseases like age-related macular degeneration and/or diabetic retinopathy. Most of the works established AI-based predicting models using images taken by fundus cameras and/or optical coherence tomography (OCT) devices to capture disease-related structural disorders. In contrast, the interference fringes dynamically change the colors and patterns spatiotemporally. To the best of our knowledge, there is no AI-based model studied for distinguishing ADDE and EDE using interference fringe images. However, an AI-based study classifying the condition of the tear lipid layer by analyzing the textures of interference fringes compared to the device-unique grades has been reported. This suggested the possibility of using the unstructured characteristics, such as colors and/or complexities of interference fringes, as the numerical image features when building AI-based prediction models. In this study, we first examined several types of image characteristics extracted from the colors and patterns of fringes to obtain effective image features for the DED-type classification. We then evaluated whether the AI-based models would have sufficient abilities for this type of prediction by comparing their diagnoses with those made by an ophthalmologist skilled in this classification (the founder of this type classification).

7-beam lattice images of (110) oriented Ge

1978 ◽  
Vol 36 (1) ◽  
pp. 290-291
Author(s):  
J.R. Parsons ◽  
C.W. Hoelke
Keyword(s):  
Image Features ◽  
Objective Lens ◽  
Lattice Plane ◽  
Lattice Image ◽  
Crystalline Defects ◽  
Transmission Electron ◽  
Lattice Images ◽  
Electron Microscopes ◽  
Structural Aspects ◽  
Beam Lattice

The direct imaging of a crystal lattice has intrigued electron microscopists for many years. What is of interest, of course, is the way in which defects perturb their atomic regularity. There are problems, however, when one wishes to relate aperiodic image features to structural aspects of crystalline defects. If the defect is inclined to the foil plane and if, as is the case with present 100 kV transmission electron microscopes, the objective lens is not perfect, then terminating fringes and fringe bending seen in the image cannot be related in a simple way to lattice plane geometry in the specimen (1).The purpose of the present work was to devise an experimental test which could be used to confirm, or not, the existence of a one-to-one correspondence between lattice image and specimen structure over the desired range of specimen spacings. Through a study of computed images the following test emerged.

High-resolution electron microscopy of the atomic structure of some grain boundaries in Au

1986 ◽  
Vol 44 ◽  
pp. 414-415
Author(s):  
W. Krakow ◽  
D. A. Smith
Keyword(s):  
Atomic Structure ◽  
High Resolution Electron Microscopy ◽  
Image Features ◽  
Image Feature ◽  
Resolution Electron ◽  
Tilt Boundaries ◽  
Processing Techniques ◽  
Atom Position ◽  
Structure Configuration

The successful determination of the atomic structure of [110] tilt boundaries in Au stems from the investigation of microscope performance at intermediate accelerating voltages (200 and 400kV) as well as a detailed understanding of how grain boundary image features depend on dynamical diffraction processes variation with specimen and beam orientations. This success is also facilitated by improving image quality by digital image processing techniques to the point where a structure image is obtained and each atom position is represented by a resolved image feature. Figure 1 shows an example of a low angle (∼10°) Σ = 129/[110] tilt boundary in a ∼250Å Au film, taken under tilted beam brightfield imaging conditions, to illustrate the steps necessary to obtain the atomic structure configuration from the image. The original image of Fig. 1a shows the regular arrangement of strain-field images associated with the cores of ½ [10] primary dislocations which are separated by ∼15Å.

Imaging of polymer single crystals in low-voltage, high-resolution scanning electron microscopy

1990 ◽  
Vol 48 (4) ◽  
pp. 1106-1107
Author(s):  
W.W. Adams ◽  
G. Price ◽  
A. Krause
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
High Resolution ◽  
Single Crystals ◽  
Low Voltage ◽  
Polymer Surface ◽  
Beam Current ◽  
Image Features ◽  
First Results ◽  
Scanning Electron

It has been shown that there are numerous advantages in imaging both coated and uncoated polymers in scanning electron microscopy (SEM) at low voltages (LV) from 0.5 to 2.0 keV compared to imaging at conventional voltages of 10 to 20 keV. The disadvantages of LVSEM of degraded resolution and decreased beam current have been overcome with the new generation of field emission gun SEMs. In imaging metal coated polymers in LVSEM beam damage is reduced, contrast is improved, and charging from irregularly shaped features (which may be unevenly coated) is reduced or eliminated. Imaging uncoated polymers in LVSEM allows direct observation of the surface with little or no charging and with no alterations of surface features from the metal coating process required for higher voltage imaging. This is particularly important for high resolution (HR) studies of polymers where it is desired to image features 1 to 10 nm in size. Metal sputter coating techniques produce a 10 - 20 nm film that has its own texture which can obscure topographical features of the original polymer surface. In examining thin, uncoated insulating samples on a conducting substrate at low voltages the effect of sample-beam interactions on image formation and resolution will differ significantly from the effect at higher accelerating voltages. We discuss here sample-beam interactions in single crystals on conducting substrates at low voltages and also present the first results on HRSEM of single crystal morphologies which show some of these effects.

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