| Application Number | Title |
|---|---|
| 1 | Effect of Computer-Aided Detection Prompts on Breast Screening Performance |
| 2 | A multicentre international evaluation of the assessment systems currently used for assessing mammographic image quality in breast screening |
| 3 | Product X - Computerized reading of mammograms and digital breast tomosynthesis |
| 4 | Structural and statistical regularities that expert radiologists extract allowing them to assess the normality |
| 5 | Computer Aided Monitoring |
| 6 | Mammography Classification and Segmentation with ConvNets |
| 9 | Developing a Convolution Neuronal Network for Cancer Detection in Mammograms |
| 11 | Deep Learning for Mammography Screening / PRECISION |
| 13 | Machine Learning for Detection, Characterisation and Risk Stratification of Lesions in Breast Imaging |
| 14 | Mammographic CAD device testing using computationally inserted microcalcification clusters and masses |
| 15 | Deep learning approaches to digital breast tomosynthesis screening mammography interpretation |
| 16 | Image quality measurement from clinical digital breast images |
| 17 | Prediction and early detection of breast cancer from screening mammograms using deep learning |
| 18 | Deep learning for smart mammography screening |
| 19 | Semi-automated mammography with Machine Learning. |
| 20 | Automatic Detection of Suspicious Lesions of Breast Cancer in Mammograms via Deep Learning |
| 22 | Validation of Dual DCNN malignancy risk classifier |
| 25 | Artificial Intelligence Algorithm applied to automated screening mammograms analysis |
| 27 | Competition X: Training images for participants |
| 30 | Classification of micro-calcifications |
| 33 | Investigation on image and lesion features that affect mammography interpretation |
| 35 | Automated Detection and Diagnosis of Breast Cancer based on Mammography Images |
| 43 | Calibration and Validation of Deep Learning Algorithms for Improved Detection of Breast Cancer in Population Screening |
| 44 | Company X Mammograpnhy Product Development |
| 47 | Smart Elimination of normal cases in breast cancer screening |
| 48 | Radiomics & deep learning can aid classification of masses on mammograms |
| 49 | Investigation into the practicalities of deploying Deep Learning models within a screening programme |
| 51 | An evaluation of Deep Learning approaches to classify breast arterial calcification from 2D digital mammography images |
| 53 | Development of machine learning method of identifying underlying tumour genotype of early stage breast cancer using mammography |
| 54 | A Deep Learning Approach for the Analysis of Masses in Mammograms |
| 55 | Company X Imaging Care Advisor for Breast |
| 61 | Company X Machine Learning Computer Aided Detection Software |
| 65 | The gist of the abnormal in prior and current mammographic images |
| 66 | Automatic Analysis and Diagnosis of Mammography |
| 69 | Interpretable and robust deep learning for breast cancer screening exam classification |
| 71 | An AI system for real-time risk assessment at mammography screening |
| 73 | Creating ML algorithm for malignancy detection on mammography studies |
| 74 | Image Computing for Enhancing Breast Cancer Radiomics |
| 80 | Predicting the severity and detecting the early stages of breast cancer by analysing mammograms with Deep Learning Algorithms |
| 84 | Attention based multiview detection of masses in mammograms |
| 85 | 3D reconstruction to improve deep learning prediction for 2D mammograms |
| 90 | Comparison of AI-Assisted breast cancer screening between DBT & 2D mammography |
| 93 | Geometic Deep Learning approaches for CADx in digital mammography |
| 94 | Using collective intelligence to differentiate between benign from malignant breast lesions |
| 98 | Deep Learning Based Computer Aided Breast Lesion Diagnosis System |
| 101 | A European Cancer Image Platform linked to Biological and Health Data for next generation AI & Precision medicine in Oncology |
| 102 | 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 |
| 118 | Studying volumetric breast density of women undergoing mammographic screening |
| 106 | To identify women with difficult to-detect or masked cancers |
| 123 | Deep learning to detect breast cancer in women with high density mammary glands |
| 128 | Using AI algorithms to assist with cancer screening |
| 129 | To improve effectiveness of breast screening through computational solutions and human performance using gist signal |
| 133 | Using screening mammograms and corresponding time-to-event data to train a deep neural networks |
| 134 | Deep learning models to assist clinicians with their decisions |
| 130 | To develop breast cancer risk predictions models |
| 136 | To evaluate AI models in order to standardise research in deep learning for screening mammography |
| 139 | A pending patented method will be used analyse mammograms for tissue disruption levels over time |
| 141 | A study investigating cancer detection across large and small malignancies. |
| 143 | To re-validate the performance of a software that can diagnose breast legions. |
| 148 | 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 |
| 152 | To create maps that describe the progression of breast malignancies, for modelling and risk estimation. |
| 156 | Developing breast cancer screening algorithms from 2D mammogram studies from a number of manufactures and devices |
| 160 | Developing AI based on deep neural networks for early detection of cancer from a series of digital mammography images |
| 161 | To develop a cloud-based image processing tool, validate it against the standard tool, and optimize processing for various vendor-specific instances using OPTIMAM. |
| 165 | To evaluate deep learning for mammogram triage, risk prediction in developing countries, and compare commercial AI network performance. |
| 166 | To develop robust and effective model for mammogram analysis for early breast cancer detection and diagnosis. |
| 169 | To develop a new AI model that can detect malignant lesions through a prospective study in Vietnamese population |
| 170 | Training the AI model for predicting upstaging using images and breast biopsies |
| 176 | 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. |
| 183 | To test and internally validate an image processing software and to calculate cancer risk predictions |
| 184 | External validation of long-term breast cancer risk with OPTIMAM digital mammograms |
| 186 | To meet statistical accuracy for commercial viability to train the underlying commercial AI model |
| 187 | To make the Licensed Product widely available and certified for use in patient care using FDA validation protocols |
| 188 | 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. |
| 189 | To introduce patient-radiologist clustering, develop an equitable medical image analysis system, and propose new evaluation measures for mammogram analysis |