156 citations · 213 across the 14 of their papers we have counts for
14 papers
LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset
Yiping Jiao, Jeroen van der Laak, Shadi Albarqouni +18
We introduce LYSTO, the Lymphocyte Assessment Hackathon, which was held in conjunction with the MICCAI 2019 Conference in Shenzen (China). The competition required participants to…
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…
NuClick: From Clicks in the Nuclei to Nuclear Boundaries
Mostafa Jahanifar, Navid Alemi Koohbanani, Nasir Rajpoot
Best performing nuclear segmentation methods are based on deep learning algorithms that require a large amount of annotated data. However, collecting annotations for nuclear segmen…
CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images
Yanning Zhou, Simon Graham, Navid Alemi Koohbanani +3
Colorectal cancer (CRC) grading is typically carried out by assessing the degree of gland formation within histology images. To do this, it is important to consider the overall tis…
Nuclear Instance Segmentation using a Proposal-Free Spatially Aware Deep Learning Framework
Navid Alemi Koohbanani, Mostafa Jahanifar, Ali Gooya +1
Nuclear segmentation in histology images is a challenging task due to significant variations in the shape and appearance of nuclei. One of the main hurdles in nuclear instance segm…
Methods for Segmentation and Classification of Digital Microscopy Tissue Images
Quoc Dang Vu, Simon Graham, Minh Nguyen Nhat To +11
High-resolution microscopy images of tissue specimens provide detailed information about the morphology of normal and diseased tissue. Image analysis of tissue morphology can help…
Segmentation of Skin Lesions and their Attributes Using Multi-Scale Convolutional Neural Networks and Domain Specific Augmentations
Mostafa Jahanifar, Neda Zamani Tajeddin, Navid Alemi Koohbanani +2
Computer-aided diagnosis systems for classification of different type of skin lesions have been an active field of research in recent decades. It has been shown that introducing le…
Nuclei Detection Using Mixture Density Networks
Navid Alemi Koohababni, Mostafa Jahanifar, Ali Gooya +1
Nuclei detection is an important task in the histology domain as it is a main step toward further analysis such as cell counting, cell segmentation, study of cell connections, etc.…
Context-Aware Learning using Transferable Features for Classification of Breast Cancer Histology Images
Ruqayya Awan, Navid Alemi Koohbanani, Muhammad Shaban +2
Convolutional neural networks (CNNs) have been recently used for a variety of histology image analysis. However, availability of a large dataset is a major prerequisite for trainin…