The number of deaths from cancers worldwide is staggering—;million deaths in 2020 according to the WHO! In the EU alone 20% of all deaths are due to cancer each year. This project  is driven and motivated to help fight these various forms of cancers around the world using advanced and innovative software tools for fast and effective early diagnosis and ;hope to contribute to the fight for eradicating and preventing cancers by collaborating with medical professionals and researchers worldwide. Using Deep Learning (DL) and Machine Learning (ML) platforms,this project will develop specialized algorithmic solutions to analyze medical images for oncology and radiology for faster and better than human accuracy. These  solutions, which are designed to offer faster and better access for second and third diagnostic opinions by medical professionals to help develop a therapeutic approach quickly. This will help the patients and physicians by reducing anxiety caused by not knowing what they are dealing with.

My project idea(proposals) is following:

• Reseasrch topic 1: Can attention-like mechanisms help in a semantic appreciation of cancer imaging data?

If a patient is suspected of having cancer, a tissue sample may be taken for further investigation. This biopsy is processed and scanned to yield a high-resolution digital image which is examined by a histopathologist. My first idea will explore whether Artificial Intelligence can support the pathologist in reaching a diagnosis. The problem is that existing AI algorithms would struggle to process such large images quickly and accurately. This project will examine attention in artificial neural networks, to see whether emulating visual attention processes in the human eye and brain can identify areas of the biopsy image that are most likely to contain cancer cells. These regions can then be examined in greater detail, to deliver a more accurate diagnosis more rapidly.

• Research Topic 2: Using AI to combine genomic with pathology images to improve lymphoma diagnosis

In this project I will be looking at lymphoma image and genetic data, with the intention to contribute to the understanding of different sub-types of lymphoma and to improve diagnosis. Lymphoma arises from excessive mutations in lymphocytes. The pathogenic variants often occur during lymphocyte differentiation, where induced variation is crucial. Currently, researchers have defined over 60 sub-types of lymphoma, although these are usually grouped into broader categories. In this project I hope to utilise a range of tools based on deep learning and supervised and unsupervised techniques to identify lymphoma sub-groups and better understand the disease. For this, over 1500 data samples will be made available. Each data sample has genetic data and image data, where each image of the tissue sample is high resolution, featuring microscopic detail, resolving features down to cell nuclei.

• Research Topic 3: Deep learning for lung cancer detection and cancer risk stratification by integration of imaging and electronic medical records

Lung cancer is the leading cause of cancer related deaths in Germany with very low five- and ten-year survival rates. This is attributed to the fact that the cancer is typically diagnosed at an advanced stage as early detection is particularly challenging. Consequently, there is a clear need for automated and robust systems that facilitate its early detection, diagnosis and treatment. The goal of this project is to develop a system to improve patient management in the context of both lung cancer screening and incidental nodule discovery. The project objectives are defined to this end:

• Develop a fully supervised lung nodule detection framework for automatic assessment of low dose chest CTs
• Develop a cancer risk stratification system based on the official clinical guidelines.
• Investigate the use of NLP tools for extracting useful information from electronic medical records (EMRs), for subsequent integration with the image analytics.
• Develop a weakly supervised nodule detection algorithm for the effective integration of NLP-derived data from radiology reports with imaging data (low-dose chest CTs).

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 Medical Biotechnology

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