Aïcha BenTaieb

Ph.D. Student
Medical Image Analysis Lab
School of Computing Science
Simon Fraser University
BC, Canada
abentaie [at] sfu [dot] ca




About

Hi!
I am currently a Ph.D. student in the Medical Image Analysis Lab at Simon Fraser University, supervised by Prof. Ghassan Hamarneh. My research involves designing computational models that can facilitate cancer diagnosis from histopathologic images of tissue biopsies.

Research Interests

News

  • June 2018
    New MICCAI 2018 paper: Predicting Cancer with a Recurrent Visual Attention Model for Histopathology Images.
  • Dec 2017
    New journal paper to appear in TMI 2017: Adversarial Stain Transfer for Histopathology Image Analysis.
  • Oct 2017
    New poster presentation at SFU's Women in Health Symposium, 2017: Artificial Pathologists: Machine Learning Models for Histopathology.
  • June 2017
    New article in MICCAI 2017 LABELS Workshop: Uncertainty Driven Multi Loss Fully Convolutional Networks for Histopathology.
  • May. 2017
    New article published in the Journal of Medical Image Analysis: A Structured Latent Model for Ovarian Carcinoma Subtyping from Histopathology Slides
  • Oct. 2016
    Interviewed as "women in science" by the MICCAI 2016 Daily Magazine.
  • April 2016
    Our paper on "Topology-Aware Fully Convolutional Networks for Histology Gland Segmentation" was accepted at MICCAI 2016 and long-listed for MICCAI's young scientist award.
  • April 2016
    Our paper on "Multi-Loss Convolutional Networks for Gland Analysis in Microscopy" was accepted as oral at the ISBI'16 Deep Learning Session, in Prague. [Teaser]
  • Feb. 2016
    New WEB-API for automatic diagnosis of ovarian carcinoma digital histopathology slides: [LINK] .
  • Jan. 2016
    New paper on "Clinically-Inspired Automatic Diagnosis of Ovarian Carcinoma Subtypes" to appear in Journal of Pathology Informatics'16.
  • June 2015
    Our work on ovarian carcinomas diagnosis was featured on the frontpage of the Vancouver Sun!
  • May 2015
    Check out our teaser video for our paper on "Automatic Diagnosis of Ovarian Carcinomas via Sparse Multiresolution Tissue Representation" to appear in MICCAI'15.
  • April 2015
    Our paper "Automatic Diagnosis of Ovarian Carcinomas via Sparse Multiresolution Tissue Representation" got accepted to MICCAI'15.
  • Oct. 2014
    We've presented our work on " Computer Vision and Machine Learning in Digital Pathology: Teaching Computers to Interpret Histopathology Images" at OVCARE (BC's Ovarian Cancer Research Team).

Publications

A. BenTaieb and G. Hamarneh. "Predicting Cancer with a Recurrent Visual Attention Model for Histopathology Images". MICCAI 2018.
[pdf][code]
A. BenTaieb and G. Hamarneh. "Adversarial Stain Transfer for Histopathology Image Analysis". TMI 2017.
[pdf][code]
A. BenTaieb and G. Hamarneh. "Artificial Pathologists: Machine Learning Models for Histopathology.". In Annual SFU Health Research Day - Women's Health Research Symposium, 2017.
[poster]
A. BenTaieb and G. Hamarneh. "Uncertainty Driven Multi Loss Fully Convolutional Networks for Histopathology". MICCAI LABELS Workshop 2017.
[pdf] [poster][code]
A. BenTaieb, H. Li-Chang, D. Huntsman and G. Hamarneh. "A Structured Latent Model for Ovarian Carcinoma Subtyping from Histopathology Slides.". MIA 2017.
[pdf] [code]
A. BenTaieb and G. Hamarneh. "Topology-Aware Fully Convolutional Networks for Histology Gland Segmentation". MICCAI 2016.
[pdf] [code]
A. BenTaieb, J. Kawahara and G. Hamarneh. "Multi-Loss Convolutional Networks for Gland Analysis in Microscopy". ISBI 2016.
[pdf] [teaser]
A. BenTaieb and G. Hamarneh. Histopathology WEB-API.
[LINK] [DEMO]
A. BenTaieb, M S. Nosrati, H. Li-Chang, D. Huntsman and G. Hamarneh. "Clinically-Inspired Automatic Diagnosis of Ovarian Carcinoma Subtypes". Journal of Pathology Informatics 2016.
[pdf] [teaser]
A. BenTaieb, H. Li-Chang, D. Huntsman and G. Hamarneh. "Automatic Diagnosis of Ovarian Carcinomas via Sparse Multiresolution Tissue Representation". MICCAI 2015.
[pdf] [teaser]