About

I recieved my B.Sc. and M.Sc. in computer engineering from Sharif University of Technology. I’m interested in machine learning, computer vision, and image processing. I’m currently a PhD student at University of Alberta. I’m supervised by professor Nilanjan Ray from Computing Science department and professor Gilbert Bigras from cross-cancer institue (CCI) and UofA department of Pathology.

Email: ah8@ualberta.ca

Office: 111 Athabasca Hall.

Research

In the past, cancer diagnosis was based on viewing tissues/cells under microscope. Recently, making primary diagnosis from scanned/digitized pathology images has been officially approved. Apart from its clinical benefits, digital pathology provided machine learning researchers with abundant of data.

My research is about developing/adopting machine learning methods for digitized pathology images.

Code

We’ve developed a tool we called PyDmed to facilitate developing machine learning methods for whole-slide images and medical images in general. If you work on ML + medical imaging, PyDmed is really a life changer (research changer :) ).

You can check out PyDmed’s repository on github.

Publications

• N. Guruprasad, A. Akbarnejad, PJ Barnes, G. Bigras, and N. Ray, “A Closer Look at Weak Supervision’s Limitations in WSI Recurrence Score Prediction”, 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2023).

• A. Akbarnejad and N. Ray and P. J Barnes and G. Bigras, “Predicting Ki67, ER, PR, and HER2 Statuses from H&E-stained Breast Cancer Images,” arxiv preprint: https://arxiv.org/abs/2308.01982.

Link to the datasetihc4bc.githubio

• A. Akbarnejad and G. Bigras and N. Ray, “GPEX, A Framework For Interpreting Artificial Neural Networks,” Conference on Neural Information Processing Systems (NeurIPS) 2023.

Link to the github repo https://github.com/amirakbarnejad/gpex

Link to GPEX documentation https://gpex.readthedocs.io/en/latest/

• Y. Yang and A. Akbarnejad and N. Ray and G. Bigras, “Double adversarial domain adaptation for whole-slide-imageclassification,” Medical Imaging with Deep Learning (MIDL), 2021.

• A. Akbarnejad and N. Ray and G. Bigras, “Deep Fisher Vector Coding For Whole Slide Image Classification,” in International Symposium of Biomedical Imaging (ISBI), 2021.

Code on github:https://github.com/amirakbarnejad/code_submission_isbi2021

• A. Akbarnejad and M. Soleymani Baghshah, “An Efficient Semi-supervised Multi-label Classi- fier Capable of Handling Missing Labels,” in IEEE Transactions on Knowledge and Data Engineering, 2018.

• A. Akbarnejad and M. Soleymani Baghshah, “A Probabilistic Multi-label Classifier with Miss- ing and Noisy Labels Handling Capability.,” in Pattern Recognition Letters, 2017.