Prof. Asoke Nandi

Head of Electronic & Computer Engineering Department at Brunel University London, United Kingdom

Breast Cancer Detection from Images with Artificial Intelligence

Talk Abstract: Breast cancer is the most common form of cancer amongst women. It is a worldwide problem and a growing one. The data in this presentation will come from breast images and the problem is to detect breast cancer. First, this talk will introduce the problem and highlight some technical challenges. Second, some attempts at breast cancer detection from mammographic images will be outlined and some results will be presented. This presentation will introduce a number of topics from artificial intelligence like feature selection, feature generation, and classification. These will be used in conjunction with image processing to address computer-aided breast cancer detection successfully.

Biography: Professor Asoke K. Nandi received the degree of Ph.D.in Physics from the University of Cambridge (Trinity College), Cambridge. He held academic positions in several universities, including Oxford and Imperial College London, as well as Finland Distinguished Professorship in Jyvaskyla. In 2013 he moved to Brunel University London. Professor Nandi is a Distinguished Visiting Professor at Tongji University and an Adjunct Professor at the University of Calgary. In 1983 Professor Nandi co-discovered the three fundamental particles known as W+, W−and Z0, for which two of his team leaders were awarded the 1984 Nobel Prize for Physics their decisive contributions. His current research interests are in signal processing and machine learning, with applications to functional magnetic resonance data, gene expression data, communications, and biomedical data. He has authored over 550 technical publications, including 220 journal papers and four books. The h-index of his publications is 67 (Google Scholar) and ERDOS number is 2.Professor Nandi is a Fellow of the Royal Academy of Engineering and also a Fellow of seven other institutions including the IEEE. Among the many awards he received are the Institute of Electrical and Electronics Engineers Heinrich Hertz Award in 2012, the Glory of Bengal Award for his outstanding achievements in scientific research in 2010, the Water Arbitration Prize of the Institution of Mechanical Engineers in 1999, and the Mountbatten Premium, Division Award of the Electronics and Communications Division, of the Institution of Electrical Engineers in 1998. Professor Nandi is an IEEE EMBS Distinguished Lecturer (2018-19). 

Dalia Mahdy

A Research Assistant with the Medical Micro & Nano Robotics Lab of the German University in Cairo, Egypt.

Talk Abstract: The deployment of micro-robotics in biomedical applications can revolutionize medicine and technology, owing to their small size and their potential to reach locations inside the human body to achieve non-trivial tasks that are not possible using traditional therapeutic interventions. Also, to avoid the complications and risks associated with surgical procedures. The greatest power of these micro-robots in biomedical applications has emerged when clinical imaging modalities were incorporated to provide feedback and enable accurate in vivo tracking and control towards a desired position in three-dimensional space.

In our work, we present a promising potential biomedical application which is the mechanical rubbing of blood clots using helical robots. The continuous interaction between the tip of the helical robot and the fibrin network of the clot has proven to be efficient in decreasing the size of the clot in vitro. However, the translation of this concept into in vivo trials requires several challenges to be overcome. Among these challenges is the need to track and controllably navigate the helical robot using a medical imaging modality. Therefore, we present the navigation of the helical robot in a one-dimensional channel using ultrasound feedback.

Wubshet Shimels Negussie

A Master’s Student at System & Biomedical Engineering Department at Cairo University in Giza, Egypt.

Talk Abstract: Malaria is the leading cause of morbidity and mortality in tropical and subtropical countries. The diagnosis is usually done manually by conventional light microscopy which has occasionally proved inefficient since it is laborious, time consuming and subjective. All diagnosis techniques which yield better results are quite expensive and hence inaccessible to the developing countries where this disease is more endemic. To support manual methods, we developed a system which can automatically detect the plasmodium parasites from thin blood smear images. We used a dataset provided by the Centers for Disease Control and Prevention (CDC). Although automating the process is a good solution, the available techniques are unable to evaluate the same cases at which the morphology of red blood cells (RBCs) is affected by diseases such as anemia and hemoglobinopathies. In this work, we proposed an accurate, rapid and affordable system for malaria diagnosis. The proposed method applies image preprocessing with bilateral filters, followed by the contrast-limited adaptive histogram equalization (CLAHE), and color-based segmentation of parasites through an adaptive Gaussian mixture model (GMM). We investigated the detection performance with color features from the CIElab and HSI color spaces in addition to the RGB colour model. Then, occluded RBCs were separated using the distance transform, and local maxima of the distance map where detected as the centers of the RBC cells. Overlapping cells can then be separated using the shortest-path methods. Further, the classification and counting of infected and non-infected RBCs have been made. The experimental results demonstrated that the developed method had a good performance (98.98% accuracy, 95.16% sensitivity and 99.58% specificity) with respect to the ground truth data. Eventually, this work can be considered as a low-cost solution for the malaria quantification in lower income countries and massive examinations.

Mohamed Emam

A Research Assistant, Center of Informatics Science, Bioinformatics Group, Nile University, Egypt.

Monitoring The Evolution Pattern Of Mammals Lung Adenocarcinoma Genes

Biography: Graduated from Biotechnology Department, Faculty of Agriculture Cairo University, Giza(Egypt). He worked six months as an intern research assistant in Evolutionary genomics, Bioinformatics group at Faculty of Science, University of Porto, Portugal. And He currently is working as a research assistant at center of informatics science, Bioinformatics group, Nile University, Egypt.