Table of Applicants

Application Number Title
1Effect of Computer-Aided Detection Prompts on Breast Screening Performance
2A multicentre international evaluation of the assessment systems currently used for assessing mammographic image quality in breast screening
3Product X - Computerized reading of mammograms and digital breast tomosynthesis
4Structural and statistical regularities that expert radiologists extract allowing them to assess the normality
5Computer Aided Monitoring
6Mammography Classification and Segmentation with ConvNets
9Developing a Convolution Neuronal Network for Cancer Detection in Mammograms
11Deep Learning for Mammography Screening / PRECISION
13Machine Learning for Detection, Characterisation and Risk Stratification of Lesions in Breast Imaging
14Mammographic CAD device testing using computationally inserted microcalcification clusters and masses
15Deep learning approaches to digital breast tomosynthesis screening mammography interpretation
16Image quality measurement from clinical digital breast images
17Prediction and early detection of breast cancer from screening mammograms using deep learning
18Deep learning for smart mammography screening
19Semi-automated mammography with Machine Learning.
20Automatic Detection of Suspicious Lesions of Breast Cancer in Mammograms via Deep Learning
22Validation of Dual DCNN malignancy risk classifier
25Artificial Intelligence Algorithm applied to automated screening mammograms analysis
27Competition X: Training images for participants
30Classification of micro-calcifications
33Investigation on image and lesion features that affect mammography interpretation
35Automated Detection and Diagnosis of Breast Cancer based on Mammography Images
43Calibration and Validation of Deep Learning Algorithms for Improved Detection of Breast Cancer in Population Screening
44Company X Mammograpnhy Product Development
47Smart Elimination of normal cases in breast cancer screening
48Radiomics & deep learning can aid classification of masses on mammograms
49Investigation into the practicalities of deploying Deep Learning models within a screening programme
51An evaluation of Deep Learning approaches to classify breast arterial calcification from 2D digital mammography images
53Development of machine learning method of identifying underlying tumour genotype of early stage breast cancer using mammography
54A Deep Learning Approach for the Analysis of Masses in Mammograms
55Company X Imaging Care Advisor for Breast
61Company X Machine Learning Computer Aided Detection Software
65The gist of the abnormal in prior and current mammographic images
66Automatic Analysis and Diagnosis of Mammography
69Interpretable and robust deep learning for breast cancer screening exam classification
71An AI system for real-time risk assessment at mammography screening
73Creating ML algorithm for malignancy detection on mammography studies
74Image Computing for Enhancing Breast Cancer Radiomics
80Predicting the severity and detecting the early stages of breast cancer by analysing mammograms with Deep Learning Algorithms
84Attention based multiview detection of masses in mammograms
853D reconstruction to improve deep learning prediction for 2D mammograms
90Comparison of AI-Assisted breast cancer screening between DBT & 2D mammography
93Geometic Deep Learning approaches for CADx in digital mammography
94Using collective intelligence to differentiate between benign from malignant breast lesions
98Deep Learning Based Computer Aided Breast Lesion Diagnosis System
101A European Cancer Image Platform linked to Biological and Health Data for next generation AI & Precision medicine in Oncology
102Combining 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
118Studying volumetric breast density of women undergoing mammographic screening
106To identify women with difficult to-detect or masked cancers
123Deep learning to detect breast cancer in women with high density mammary glands
128Using AI algorithms to assist with cancer screening
129To improve effectiveness of breast screening through computational solutions and human performance using gist signal
133Using screening mammograms and corresponding time-to-event data to train a deep neural networks
134Deep learning models to assist clinicians with their decisions
130To develop breast cancer risk predictions models
136To evaluate AI models in order to standardise research in deep learning for screening mammography
139A pending patented method will be used analyse mammograms for tissue disruption levels over time
141A study investigating cancer detection across large and small malignancies.
143To re-validate the performance of a software that can diagnose breast legions.
148To 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
152To create maps that describe the progression of breast malignancies, for modelling and risk estimation.
156Developing breast cancer screening algorithms from 2D mammogram studies from a number of manufactures and devices
160Developing AI based on deep neural networks for early detection of cancer from a series of digital mammography images
161To develop a cloud-based image processing tool, validate it against the standard tool, and optimize processing for various vendor-specific instances using OPTIMAM.
162To Predict the risk of Breast Cancer by Combining multiple imaging modalities in Convolutional Neural Networks
165To evaluate deep learning for mammogram triage, risk prediction in developing countries, and compare commercial AI network performance.
166To develop robust and effective model for mammogram analysis for early breast cancer detection and diagnosis.
169To develop a new AI model that can detect malignant lesions through a prospective study in Vietnamese population
170Training the AI model for predicting upstaging using images and breast biopsies
176Breast 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.
183To test and internally validate an image processing software and to calculate cancer risk predictions
184External validation of long-term breast cancer risk with OPTIMAM digital
186To meet statistical accuracy for commercial viability to train the underlying commercial AI model
187To make the Licensed Product widely available and certified for use in patient care using FDA validation protocols
188To develop a lightweight CNN model for classification of Microcalcifications Clusters (MCCs) in mammogram patches on annotated MCC regions, and using data augmentation for training.
189To introduce patient-radiologist clustering, develop an equitable medical image analysis system, and propose new evaluation measures for mammogram analysis