156 citations · 176 across the 6 of their papers we have counts for
13 papers
Weakly-supervised learning for image-based classification of primary melanomas into genomic immune subgroups
Lucy Godson, Navid Alemi, Jeremie Nsengimana +6
Determining early-stage prognostic markers and stratifying patients for effective treatment are two key challenges for improving outcomes for melanoma patients. Previous studies ha…
Robust Interactive Semantic Segmentation of Pathology Images with Minimal User Input
Mostafa Jahanifar, Neda Zamani Tajeddin, Navid Alemi Koohbanani +1
From the simple measurement of tissue attributes in pathology workflow to designing an explainable diagnostic/prognostic AI tool, access to accurate semantic segmentation of tissue…
Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotations
Navid Alemi Koohbanani, Balagopal Unnikrishnan, Syed Ali Khurram +2
While high-resolution pathology images lend themselves well to `data hungry' deep learning algorithms, obtaining exhaustive annotations on these images is a major challenge. In thi…
Multi-Task Learning in Histo-pathology for Widely Generalizable Model
Jevgenij Gamper, Navid Alemi Kooohbanani, Nasir Rajpoot
In this work we show preliminary results of deep multi-task learning in the area of computational pathology. We combine 11 tasks ranging from patch-wise oral cancer classification,…
NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Images
Navid Alemi Koohbanani, Mostafa Jahanifar, Neda Zamani Tajadin +1
Object segmentation is an important step in the workflow of computational pathology. Deep learning based models generally require large amount of labeled data for precise and relia…
PanNuke Dataset Extension, Insights and Baselines
Jevgenij Gamper, Navid Alemi Koohbanani, Ksenija Benes +6
The emerging area of computational pathology (CPath) is ripe ground for the application of deep learning (DL) methods to healthcare due to the sheer volume of raw pixel data in who…