387 citations · 2k across the 49 of their papers we have counts for
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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…
HydraMix-Net: A Deep Multi-task Semi-supervised Learning Approach for Cell Detection and Classification
R. M. Saad Bashir, Talha Qaiser, Shan E Ahmed Raza +1
Semi-supervised techniques have removed the barriers of large scale labelled set by exploiting unlabelled data to improve the performance of a model. In this paper, we propose a se…
SAFRON: Stitching Across the Frontier for Generating Colorectal Cancer Histology Images
Srijay Deshpande, Fayyaz Minhas, Simon Graham +1
Synthetic images can be used for the development and evaluation of deep learning algorithms in the context of limited availability of data. In the field of computational pathology,…
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…
Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images
Simon Graham, David Epstein, Nasir Rajpoot
Histology images are inherently symmetric under rotation, where each orientation is equally as likely to appear. However, this rotational symmetry is not widely utilised as prior k…
Meta-SVDD: Probabilistic Meta-Learning for One-Class Classification in Cancer Histology Images
Jevgenij Gamper, Brandon Chan, Yee Wah Tsang +2
To train a robust deep learning model, one usually needs a balanced set of categories in the training data. The data acquired in a medical domain, however, frequently contains an a…