469 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of TorontoCA29 papers
- Sunnybrook Research Institute11 papers
- University of WaterlooCA9 papers
- Health Sciences CentreCA8 papers
- McGill UniversityCA5 papers
- University College LondonGB5 papers
- University Health NetworkCA5 papers
- Imperial College LondonGB4 papers
- InsermFR4 papers
- Sunnybrook HospitalCA4 papers
- University of AlbertaCA4 papers
- University of British ColumbiaCA4 papers
13 papers · 1 filter
Towards patient-specific optimization for mandibular reconstruction planning based on predicted bone-union propensity
Hamidreza Aftabi, John E. Lloyd, Amanda Ding +4
Mandibular reconstruction with vascularized bone grafts is complicated by donor-host nonunion, and virtual surgical planning produces a geometric plan rather than optimizing for bo…
Understanding metric-related pitfalls in image analysis validation
Annika Reinke, Minu D. Tizabi, Michael Baumgartner +75
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation int…
Metrics reloaded: Recommendations for image analysis validation
Lena Maier-Hein, Annika Reinke, Patrick Godau +71
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen…
Probeable DARTS with Application to Computational Pathology
Sheyang Tang, Mahdi S. Hosseini, Lina Chen +5
AI technology has made remarkable achievements in computational pathology (CPath), especially with the help of deep neural networks. However, the network performance is highly rela…
Adapting to Unseen Vendor Domains for MRI Lesion Segmentation
Brandon Mac, Alan R. Moody, April Khademi
One of the key limitations in machine learning models is poor performance on data that is out of the domain of the training distribution. This is especially true for image analysis…
Self-supervised driven consistency training for annotation efficient histopathology image analysis
Chetan L. Srinidhi, Seung Wook Kim, Fu-Der Chen +1
Training a neural network with a large labeled dataset is still a dominant paradigm in computational histopathology. However, obtaining such exhaustive manual annotations is often…
Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification
Marc Combalia, Veronica Vilaplana
We propose a patch sampling strategy based on a sequential Monte-Carlo method for high resolution image classification in the context of Multiple Instance Learning. When compared w…
From Hand-Crafted to Deep Learning-based Cancer Radiomics: Challenges and Opportunities
Parnian Afshar, Arash Mohammadi, Konstantinos N. Plataniotis +2
Recent advancements in signal processing and machine learning coupled with developments of electronic medical record keeping in hospitals and the availability of extensive set of m…
Semi-supervised Dictionary Learning Based on Hilbert-Schmidt Independence Criterion
Mehrdad J. Gangeh, Safaa M. A. Bedawi, Ali Ghodsi +1
In this paper, a novel semi-supervised dictionary learning and sparse representation (SS-DLSR) is proposed. The proposed method benefits from the supervisory information by learnin…
Tumour ROI Estimation in Ultrasound Images via Radon Barcodes in Patients with Locally Advanced Breast Cancer
Hamid R. Tizhoosh, Mehrdad J. Gangeh, Hadi Tadayyon +1
Quantitative ultrasound (QUS) methods provide a promising framework that can non-invasively and inexpensively be used to predict or assess the tumour response to cancer treatment.…
Sparse Reconstruction of Compressive Sensing MRI using Cross-Domain Stochastically Fully Connected Conditional Random Fields
Edward Li, Farzad Khalvati, Mohammad Javad Shafiee +2
Magnetic Resonance Imaging (MRI) is a crucial medical imaging technology for the screening and diagnosis of frequently occurring cancers. However image quality may suffer by long a…
Medical Image Classification via SVM using LBP Features from Saliency-Based Folded Data
Zehra Camlica, H. R. Tizhoosh, Farzad Khalvati
Good results on image classification and retrieval using support vector machines (SVM) with local binary patterns (LBPs) as features have been extensively reported in the literatur…
Discovery Radiomics for Pathologically-Proven Computed Tomography Lung Cancer Prediction
Devinder Kumar, Mohammad Javad Shafiee, Audrey G. Chung +3
Lung cancer is the leading cause for cancer related deaths. As such, there is an urgent need for a streamlined process that can allow radiologists to provide diagnosis with greater…