The multimodality cell segmentation challenge: toward universal solutions
Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images.
Existing cell segmentation methods are often tailored to specific modalities or require …
Existing cell segmentation methods are often tailored to specific modalities or require …
Machine learning for cross-scale microscopy of viruses
Despite advances in virological sciences and antiviral research, viruses continue to emerge,
circulate, and threaten public health. We still lack a comprehensive understanding of how …
circulate, and threaten public health. We still lack a comprehensive understanding of how …
NuInsSeg: A fully annotated dataset for nuclei instance segmentation in H&E-stained histological images
A Mahbod, C Polak, K Feldmann, R Khan, K Gelles… - Scientific Data, 2024 - nature.com
In computational pathology, automatic nuclei instance segmentation plays an essential role
in whole slide image analysis. While many computerized approaches have been proposed …
in whole slide image analysis. While many computerized approaches have been proposed …
A survey on cell nuclei instance segmentation and classification: Leveraging context and attention
Nuclear-derived morphological features and biomarkers provide relevant insights regarding
the tumour microenvironment, while also allowing diagnosis and prognosis in specific …
the tumour microenvironment, while also allowing diagnosis and prognosis in specific …
Domain generalization in computational pathology: survey and guidelines
Deep learning models have exhibited exceptional effectiveness in Computational Pathology
(CPath) by tackling intricate tasks across an array of histology image analysis applications …
(CPath) by tackling intricate tasks across an array of histology image analysis applications …
DAWN: Domain-Adaptive Weakly Supervised Nuclei Segmentation via Cross-Task Interactions
Y Zhang, Y Wang, Z Fang, H Bian, L Cai… - arxiv preprint arxiv …, 2024 - arxiv.org
Weakly supervised segmentation methods have gained significant attention due to their
ability to reduce the reliance on costly pixel-level annotations during model training …
ability to reduce the reliance on costly pixel-level annotations during model training …
Deep learning for predicting prognostic consensus molecular subtypes in cervical cancer from histology images
Cervical cancer remains the fourth most common cancer among women worldwide. This
study proposes an end-to-end deep learning framework to predict consensus molecular …
study proposes an end-to-end deep learning framework to predict consensus molecular …
GrandQC: A comprehensive solution to quality control problem in digital pathology
Z Weng, A Seper, A Pryalukhin, F Mairinger… - Nature …, 2024 - nature.com
Histological slides contain numerous artifacts that can significantly deteriorate the
performance of image analysis algorithms. Here we develop the GrandQC tool for tissue and …
performance of image analysis algorithms. Here we develop the GrandQC tool for tissue and …
A novel AI-based score for assessing the prognostic value of intra-epithelial lymphocytes in oral epithelial dysplasia
Background Oral epithelial dysplasia (OED) poses a significant clinical challenge due to its
potential for malignant transformation and the lack of reliable prognostic markers. Current …
potential for malignant transformation and the lack of reliable prognostic markers. Current …
An automated pipeline for tumour-infiltrating lymphocyte scoring in breast cancer
Tumour-infiltrating lymphocytes (TILs) are considered as a valuable prognostic markers in
both triple-negative and human epidermal growth factor receptor 2 (HER2) positive breast …
both triple-negative and human epidermal growth factor receptor 2 (HER2) positive breast …