{"id":2568,"date":"2017-09-04T11:28:51","date_gmt":"2017-09-04T11:28:51","guid":{"rendered":"https:\/\/medphys.royalsurrey.nhs.uk\/omidb\/?page_id=2568"},"modified":"2017-11-09T09:21:34","modified_gmt":"2017-11-09T09:21:34","slug":"table-of-applicants","status":"publish","type":"page","link":"https:\/\/medphys.royalsurrey.nhs.uk\/omidb\/table-of-applicants\/","title":{"rendered":"Table of Applicants"},"content":{"rendered":"\n\n\n\n\t\n\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t
Application Number<\/th> Title<\/th>\n<\/tr>\n<\/thead>\n
1<\/td>Effect of Computer-Aided Detection Prompts on Breast Screening Performance<\/td>\n<\/tr>\n
2<\/td>A multicentre international evaluation of the assessment systems currently used for assessing mammographic image quality in breast screening<\/td>\n<\/tr>\n
3<\/td>Product X - Computerized reading of mammograms and digital breast tomosynthesis<\/td>\n<\/tr>\n
4<\/td>Structural and statistical regularities that expert radiologists extract allowing them to assess the normality<\/td>\n<\/tr>\n
5<\/td>Computer Aided Monitoring<\/td>\n<\/tr>\n
6<\/td>Mammography Classification and Segmentation with ConvNets<\/td>\n<\/tr>\n
9<\/td>Developing a Convolution Neuronal Network for Cancer Detection in Mammograms<\/td>\n<\/tr>\n
11<\/td>Deep Learning for Mammography Screening \/ PRECISION<\/td>\n<\/tr>\n
13<\/td>Machine Learning for Detection, Characterisation and Risk Stratification of Lesions in Breast Imaging<\/td>\n<\/tr>\n
14<\/td>Mammographic CAD device testing using computationally inserted microcalcification clusters and masses<\/td>\n<\/tr>\n
15<\/td>Deep learning approaches to digital breast tomosynthesis screening mammography interpretation<\/td>\n<\/tr>\n
16<\/td>Image quality measurement from clinical digital breast images<\/td>\n<\/tr>\n
17<\/td>Prediction and early detection of breast cancer from screening mammograms using deep learning<\/td>\n<\/tr>\n
18<\/td>Deep learning for smart mammography screening<\/td>\n<\/tr>\n
19<\/td>Semi-automated mammography with Machine Learning.<\/td>\n<\/tr>\n
20<\/td>Automatic Detection of Suspicious Lesions of Breast Cancer in Mammograms via Deep Learning<\/td>\n<\/tr>\n
22<\/td>Validation of Dual DCNN malignancy risk classifier<\/td>\n<\/tr>\n
25<\/td>Artificial Intelligence Algorithm applied to automated screening mammograms analysis<\/td>\n<\/tr>\n
27<\/td>Competition X: Training images for participants<\/td>\n<\/tr>\n
30<\/td>Classification of micro-calcifications<\/td>\n<\/tr>\n
33<\/td>Investigation on image and lesion features that affect mammography interpretation<\/td>\n<\/tr>\n
35<\/td>Automated Detection and Diagnosis of Breast Cancer based on Mammography Images<\/td>\n<\/tr>\n
43<\/td>Calibration and Validation of Deep Learning Algorithms for Improved Detection of Breast Cancer in Population Screening <\/td>\n<\/tr>\n
44<\/td>Company X Mammograpnhy Product Development<\/td>\n<\/tr>\n
47<\/td>Smart Elimination of normal cases in breast cancer screening<\/td>\n<\/tr>\n
48<\/td>Radiomics & deep learning can aid classification of masses on mammograms<\/td>\n<\/tr>\n
49<\/td>Investigation into the practicalities of deploying Deep Learning models within a screening programme<\/td>\n<\/tr>\n
51<\/td>An evaluation of Deep Learning approaches to classify breast arterial calcification from 2D digital mammography images<\/td>\n<\/tr>\n
53<\/td>Development of machine learning method of identifying underlying tumour genotype of early stage breast cancer using mammography<\/td>\n<\/tr>\n
54<\/td>A Deep Learning Approach for the Analysis of Masses in Mammograms<\/td>\n<\/tr>\n
55<\/td>Company X Imaging Care Advisor for Breast<\/td>\n<\/tr>\n
61<\/td>Company X Machine Learning Computer Aided Detection Software<\/td>\n<\/tr>\n
65<\/td>The gist of the abnormal in prior and current mammographic images<\/td>\n<\/tr>\n
66<\/td>Automatic Analysis and Diagnosis of Mammography<\/td>\n<\/tr>\n
69<\/td>Interpretable and robust deep learning for breast cancer screening exam classification<\/td>\n<\/tr>\n
71<\/td>An AI system for real-time risk assessment at mammography screening<\/td>\n<\/tr>\n
73<\/td>Creating ML algorithm for malignancy detection on mammography studies<\/td>\n<\/tr>\n
74<\/td>Image Computing for Enhancing Breast Cancer Radiomics<\/td>\n<\/tr>\n
80<\/td>Predicting the severity and detecting the early stages of breast cancer by analysing mammograms with Deep Learning Algorithms<\/td>\n<\/tr>\n
84<\/td>Attention based multiview detection of masses in mammograms<\/td>\n<\/tr>\n
85<\/td>3D reconstruction to improve deep learning prediction for 2D mammograms<\/td>\n<\/tr>\n
90<\/td>Comparison of AI-Assisted breast cancer screening between DBT & 2D mammography<\/td>\n<\/tr>\n
93<\/td>Geometic Deep Learning approaches for CADx in digital mammography<\/td>\n<\/tr>\n
94<\/td>Using collective intelligence to differentiate between benign from malignant breast lesions <\/td>\n<\/tr>\n
98<\/td>Deep Learning Based Computer Aided Breast Lesion Diagnosis System<\/td>\n<\/tr>\n
101<\/td>A European Cancer Image Platform linked to Biological and Health Data for next generation AI & Precision medicine in Oncology <\/td>\n<\/tr>\n
102<\/td>Combining end to end DL architectures with diverse histologically confirmed data in order to improve upon the state-of-the-art machine interpretation of digital mammography<\/td>\n<\/tr>\n
106<\/td>To identify women with difficult to-detect or masked cancers<\/td>\n<\/tr>\n
118<\/td>Studying volumetric breast density of women undergoing mammographic screening<\/td>\n<\/tr>\n
123<\/td>Deep learning to detect breast cancer in women with high density mammary glands<\/td>\n<\/tr>\n
128<\/td>Using AI algorithms to assist with cancer screening <\/td>\n<\/tr>\n
129<\/td>To improve effectiveness of breast screening through computational solutions and human performance using gist signal <\/td>\n<\/tr>\n
130<\/td>To develop breast cancer risk predictions models<\/td>\n<\/tr>\n
133<\/td>Using screening mammograms and corresponding time-to-event data to train a deep neural networks<\/td>\n<\/tr>\n
134<\/td>Deep learning models to assist clinicians with their decisions<\/td>\n<\/tr>\n
136<\/td>To evaluate AI models in order to standardise research in deep learning for screening mammography <\/td>\n<\/tr>\n
139<\/td>A pending patented method will be used analyse mammograms for tissue disruption levels over time<\/td>\n<\/tr>\n
141<\/td>A study investigating cancer detection across large and small malignancies.<\/td>\n<\/tr>\n
143<\/td>To re-validate the performance of a software that can diagnose breast legions. <\/td>\n<\/tr>\n
148<\/td>To improve the accuracy of a AI system in supporting radiologist in reading screening mammograms and breast tomosynthesis exams as well as to develop methods to assess breast cancer risk<\/td>\n<\/tr>\n
152<\/td>To create maps that describe the progression of breast malignancies, for modelling and risk estimation. <\/td>\n<\/tr>\n
156<\/td>Developing breast cancer screening algorithms from 2D mammogram studies from a number of manufactures and devices<\/td>\n<\/tr>\n
160<\/td>Developing AI based on deep neural networks for early detection of cancer from a series of digital mammography images<\/td>\n<\/tr>\n
161<\/td>To develop a cloud-based image processing tool, validate it against the standard tool, and optimize processing for various vendor-specific instances using OPTIMAM.<\/td>\n<\/tr>\n
162<\/td>To Predict the risk of Breast Cancer by Combining multiple imaging modalities in Convolutional Neural Networks<\/td>\n<\/tr>\n
165<\/td>To evaluate deep learning for mammogram triage, risk prediction in developing countries, and compare commercial AI network performance.<\/td>\n<\/tr>\n
166<\/td>To develop robust and effective model for mammogram analysis for early breast cancer detection and diagnosis. <\/td>\n<\/tr>\n
169<\/td>To develop a new AI model that can detect malignant lesions through a prospective study in Vietnamese population <\/td>\n<\/tr>\n
170<\/td>Training the AI model for predicting upstaging using images and breast biopsies<\/td>\n<\/tr>\n
176<\/td>Breast tissue density can be a challenge for Radiologists to make accurate diagnoses; this project is developing deep-learning solutions to enhance the accuracy and efficiency of identifying suspicious lesions.<\/td>\n<\/tr>\n
183<\/td>To test and internally validate an image processing software and to calculate cancer risk predictions<\/td>\n<\/tr>\n
184<\/td>External validation of long-term breast cancer risk with OPTIMAM digital
\nmammograms<\/td>\n<\/tr>\n
185<\/td>To develop an innovative AI software to detect breast lesions to support clinicians with patient treatment.<\/td>\n<\/tr>\n
186<\/td>To meet statistical accuracy for commercial viability to train the underlying commercial AI model<\/td>\n<\/tr>\n
187<\/td>To make the Licensed Product widely available and certified for use in patient care using FDA validation protocols<\/td>\n<\/tr>\n
188<\/td>To develop a lightweight CNN model for classification of Microcalcifications Clusters (MCCs) in mammogram patches on annotated MCC regions, and using data augmentation for training.<\/td>\n<\/tr>\n
189<\/td>To introduce patient-radiologist clustering, develop an equitable medical image analysis system, and propose new evaluation measures for mammogram analysis<\/td>\n<\/tr>\n
194<\/td>To identify specific types of cancer based on a large amount of data derived from breast cancer screening.<\/td>\n<\/tr>\n
199<\/td>To classify mammographic data as lesion and healthy and segregating them by classifying them on the basis of standard descriptors and evaluating different machine learning techniques and own approaches.<\/td>\n<\/tr>\n
200<\/td>To develop an automated system to diagnose breast cancer by using a huge data set obtained from across different populations around the world.<\/td>\n<\/tr>\n
202<\/td>To develop a model to diagnose breast cancer and the short-term risk of breast cancer using a large set of data.<\/td>\n<\/tr>\n
205<\/td>To use data from multiple sources for training and analysis process to detect breast lesions.<\/td>\n<\/tr>\n
207<\/td>To improve the accuracy of AI system in the detection of breast cancer using a huge collection of data.<\/td>\n<\/tr>\n
197<\/td>To develop a model to organise and analyse mammography data, making sure patients' personal information stays private and secure.<\/td>\n<\/tr>\n
211<\/td>To create a strong and reliable AI tool that can combine information from different sources to predict breast cancer progression and assess risk.<\/td>\n<\/tr>\n
203<\/td>To develop an AI system that takes mammogram images as input and automatically detects cases with no signs of breast cancer.<\/td>\n<\/tr>\n
214<\/td>The study has 2 aims. One is to improve ways to prevent breast cancer and find fast-growing tumours early. The other one is to test how well MIRAI and breast density-based screening methods work for finding breast cancer in different groups of people.<\/td>\n<\/tr>\n
221<\/td>Continued Validation and development of methods to assess breast cancer risks using mammograms.<\/td>\n<\/tr>\n
193<\/td>Develop and validate machine learning models that examine mammogram images to evaluate the likelihood of breast cancer.<\/td>\n<\/tr>\n
243<\/td>Using Artificial Intelligence to Better Detect Suspicious Changes in Mammograms by Comparing Past and Current Images<\/td>\n<\/tr>\n
234<\/td>To show that AI for breast cancer can be safely tested under stress using realistic synthetic data, helping guide future safety regulations.<\/td>\n<\/tr>\n
238<\/td>To use deep learning models to study mammogram images to find early signs of cancer, helping predict each patient\u2019s risk and giving radiologists a view of important patterns.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"saved_in_kubio":false,"footnotes":""},"class_list":["post-2568","page","type-page","status-publish","hentry"],"kubio_ai_page_context":{"short_desc":"","purpose":"general"},"_links":{"self":[{"href":"https:\/\/medphys.royalsurrey.nhs.uk\/omidb\/wp-json\/wp\/v2\/pages\/2568","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/medphys.royalsurrey.nhs.uk\/omidb\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/medphys.royalsurrey.nhs.uk\/omidb\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/medphys.royalsurrey.nhs.uk\/omidb\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/medphys.royalsurrey.nhs.uk\/omidb\/wp-json\/wp\/v2\/comments?post=2568"}],"version-history":[{"count":2,"href":"https:\/\/medphys.royalsurrey.nhs.uk\/omidb\/wp-json\/wp\/v2\/pages\/2568\/revisions"}],"predecessor-version":[{"id":2978,"href":"https:\/\/medphys.royalsurrey.nhs.uk\/omidb\/wp-json\/wp\/v2\/pages\/2568\/revisions\/2978"}],"wp:attachment":[{"href":"https:\/\/medphys.royalsurrey.nhs.uk\/omidb\/wp-json\/wp\/v2\/media?parent=2568"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}