Sulaiman Vesal

Sulaiman Vesal, M. Sc.

Researcher

Department of Computer Science
Chair of Computer Science 5 (Pattern Recognition)

Room: Room 10.136
Martensstr. 3
91058 Erlangen

Office hours

Daily Mo, Tu, We, Th, Fr,

Research Focus

  • Multi-modality cardiac image analysis and fusion
  • Computer-aided detection and diagnosis of breast cancer
  • Lesion classification in 3D MRI
  • Quantitative analysis of breast imaging modalities

  • Large-scale breast image screening and analysis

Academic CV

  • Since 01/2017
    Ph.D. candidate at the Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg
  • 08/2011 to 06/2013
    M. Sc. in Computer Science at South Asian University, India
  • 04/2007 to 12/2010
    B.Sc. in Computer Science at Kabul University, Afghanistan

Projects

Development of multi-modal, multi-scale imaging framework for the early diagnosis of breast cancer

(FAU Funds)

Project leader: ,
Project members:
Start date: March 1, 2017
End date: October 31, 2019
Extension Date: June 30, 2020

Abstract:

Breast cancer is the leading cause of cancer related deaths in women, the second most common cancer worldwide. The development and progression of breast cancer is a dynamic biological and evolutionary process. It involves a composite organ system, with transcriptome shaped by gene aberrations, epigenetic changes, the cellular biological context, and environmental influences. Breast cancer growth and response to treatment has a number of characteristics that are specific to the individual patient, for example the response of the immune system and the interaction with the neighboring tissue. The overall complexity of breast cancer is the main cause for the current, unsatisfying understanding of its development and the patient’s therapy response. Although recent precision medicine approaches, including genomic characterization and immunotherapies, have shown clear improvements with regard to prognosis, the right treatment of this disease remains a serious challenge. The vision of the BIG-THERA team is to improve individualized breast cancer diagnostics and therapy, with the ultimate goal of extending the life expectancy of breast cancer sufferers. Our primary contribution in this regard is developing a multi-modal, multi-scale framework for the early diagnosis of the molecular sub-types of breast cancer, in a manner that supplements the clinical diagnostic workflow and enables the early identification of patients compatible with specific immunotherapeutic solutions.

Publications:

Publications

2022

Journal Articles

Conference Contributions

2021

Journal Articles

Conference Contributions

2020

Journal Articles

Conference Contributions

2019

Conference Contributions

2018

Journal Articles

Conference Contributions

2017

Conference Contributions

2014

Journal Articles

Lectures

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Student Theses

Type Title Status
Project Synergistic Radiomics and CNN Features for Multiparametric MRI Lesion Classification finished
Project Dilated deeply supervised networks for hippocampus segmentation in MR finished
BA thesis Multimodal Breast Cancer Detection using a Fusion of Ultrasound and Mammogram Features finished
MA thesis Classification of Breast Density in Mammograms Using Deep Machine Learning finished
MA thesis COPD Classification in CT Images Using a 3D Convolutional Neural Network finished
MA thesis Tumor Detection & Classification in Breast Cancer Histology Images using Deep Neural Networks finished
MA thesis Deep Learning-based Denoising of Mammographic Images using Physics-driven Data Augmentation finished
MA thesis Semi-Supervised Tooth Segmentation in Dental Panoramic Radiographs Using Deep Learning finished
BA thesis Age Estimation on Panoramic Dental X-ray Images Using Deep Learning finished
MA thesis Weakly Supervised Learning for Multi-modal Breast Lesion Classification in Ultrasound and Mammogram Images finished
MA thesis Unsupervised Domain Adaptation using Adversarial Learning for Multi-model Cardiac MR Segmentation finished
MA thesis Automated Volume of Interest Reconstruction in dedicated Spiral Breast CT finished