F. (Farhan) Akram, Assistant Professor

Dr. Farhan Akram earned his Ph.D. in Computer Science from Rovira i Virgili University in 2017, with a thesis on "Active Contours for Intensity Inhomogeneous Image Segmentation," graduating cum laude under the supervision of Prof. Domenec Puig and Dr. Miguel Angel Garcia. His postdoctoral work at A*STAR in Singapore included MRI feature analysis for predicting nasopharyngeal cancer recurrence, AI-based gland segmentation for prostate cancer Gleason grade classification, and AI-driven acne grading. At Khalifa University in the UAE, he applied deep learning techniques for Alzheimer’s disease prediction. In industry, he developed computer vision solutions for microscope images at Mil-kin Inc. in Tokyo, Japan.

Currently, Dr. Akram leads the PHANTOM group at the Department of Pathology and Clinical Bioinformatics, Erasmus Medical Center, Rotterdam. His research leverages artificial intelligence to enhance diagnostic and prognostic capabilities in pathology. He develops advanced AI models for analyzing H&E, IHC, and IF whole-slide images, with a focus on cancer prognostication and molecular subtyping. Additionally, he is involved in an AI-based kidney transplant rejection prediction project. He is also interested in AI-driven 3D pathology to deepen the understanding of tumor biology and improve patient outcomes. Dr. Akram has also obtained a university teaching qualification, underscoring his commitment to advancing his teaching and supervision skills.

Dr. Akram's expertise encompasses key areas of computer science and medical imaging, with a special emphasis on applying artificial intelligence to pathology. His work combines advanced computational techniques with medical research to improve diagnostic and prognostic capabilities. He is actively involved in projects targeting a range of tumor types, including lung, breast, eye, tongue, skin, bladder, prostate, and colorectal cancers. Through these projects, Dr. Akram aims to advance understanding and treatment of various cancers by leveraging AI to enhance image analysis and prediction models.

 Publications dr. F. Akram.

1. Hanarth Funds: Histogenomic biomarker identification to improve neuroendocrine lung tumor diagnostics on biopsies, using multiplex immunohistochemistry and artificial intelligence assisted histomorphological classification (2022- 2025)
2. Hanarth Funds:  INTHYM: Artificial intelligence for histopathological classification and recurrence prediction of thymic epithelial (2023- 2026)
3. Hanarth Funds:  Response prediction to neoadjuvant chemotherapy in patients with triple negative breast cancer based on integrated diagnostics (2023- 2027)
4. ISIDORe-JRA: PATH2XNAT: COVID19 PATHOMICS MEETS XNAT (2023- 2025)
5. EMC_TKI_ LSH:  Interceptor: Artificial Intelligence based analysis of histopathology images of biopsies to predict response to neoadjuvant therapy ( 2024- 2027)
6. EMC Breakthrough Funds: Meer niertransplantaties door Artificial Intelligence-tool (2024- 2027)