162 citations · 488 across the 33 of their papers we have counts for
15 papers · 1 filter
MiTHras: Task-specific Hierarchical Semi-supervised Contrastive Masked Autoencoder for Mitotic Figure Analysis
Trinh T. L. Vuong, Simon Graham, Quoc Dang Vu +5
Mitotic figure (MF) analysis supports tumor grading and prognostic assessment, but automated models remain sensitive to differences in tissue type and image acquisition. We present…
Benchmarking Domain Generalization Algorithms in Computational Pathology
Neda Zamanitajeddin, Mostafa Jahanifar, Kesi Xu +2
Deep learning models have shown immense promise in computational pathology (CPath) tasks, but their performance often suffers when applied to unseen data due to domain shifts. Addr…
Domain generalization across tumor types, laboratories, and species -- insights from the 2022 edition of the Mitosis Domain Generalization Challenge
Marc Aubreville, Nikolas Stathonikos, Taryn A. Donovan +27
Recognition of mitotic figures in histologic tumor specimens is highly relevant to patient outcome assessment. This task is challenging for algorithms and human experts alike, with…
Why is the winner the best?
Matthias Eisenmann, Annika Reinke, Vivienn Weru +122
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to in…
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Simon Graham, Quoc Dang Vu, Mostafa Jahanifar +86
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovatio…
IMPaSh: A Novel Domain-shift Resistant Representation for Colorectal Cancer Tissue Classification
Trinh Thi Le Vuong, Quoc Dang Vu, Mostafa Jahanifar +3
The appearance of histopathology images depends on tissue type, staining and digitization procedure. These vary from source to source and are the potential causes for domain-shift…
Mitosis Detection, Fast and Slow: Robust and Efficient Detection of Mitotic Figures
Mostafa Jahanifar, Adam Shephard, Neda Zamanitajeddin +4
Counting of mitotic figures is a fundamental step in grading and prognostication of several cancers. However, manual mitosis counting is tedious and time-consuming. In addition, va…
CoNIC: Colon Nuclei Identification and Counting Challenge 2022
Simon Graham, Mostafa Jahanifar, Quoc Dang Vu +6
Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained histology images enables the extraction of interpretable cell-based features that can be…
Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge
Mostafa Jahanifar, Adam Shephard, Neda Zamani Tajeddin +5
The detection of mitotic figures from different scanners/sites remains an important topic of research, owing to its potential in assisting clinicians with tumour grading. The MItos…
Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification
Simon Graham, Mostafa Jahanifar, Ayesha Azam +14
The development of deep segmentation models for computational pathology (CPath) can help foster the investigation of interpretable morphological biomarkers. Yet, there is a major b…
Cells are Actors: Social Network Analysis with Classical ML for SOTA Histology Image Classification
Neda Zamanitajeddin, Mostafa Jahanifar, Nasir Rajpoot
Digitization of histology images and the advent of new computational methods, like deep learning, have helped the automatic grading of colorectal adenocarcinoma cancer (CRA). Prese…
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…
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.…
Supervised Saliency Map Driven Segmentation of the Lesions in Dermoscopic Images
Mostafa Jahanifar, Neda Zamani Tajeddin, Babak Mohammadzadeh Asl +1
Lesion segmentation is the first step in most automatic melanoma recognition systems. Deficiencies and difficulties in dermoscopic images such as color inconstancy, hair occlusion,…