162 citations · 176 across the 12 of their papers we have counts for
14 papers
WSI-LLaVA: A Multimodal Large Language Model for Whole Slide Image
Yuci Liang, Xinheng Lyu, Wenting Chen +8
Recent advancements in computational pathology have produced patch-level Multi-modal Large Language Models (MLLMs), but these models are limited by their inability to analyze whole…
Artificial Intelligence-Enhanced Couinaud Segmentation for Precision Liver Cancer Therapy
Liang Qiu, Wenhao Chi, Xiaohan Xing +8
Precision therapy for liver cancer necessitates accurately delineating liver sub-regions to protect healthy tissue while targeting tumors, which is essential for reducing recurrenc…
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…
Artificial intelligence for diagnosing and predicting survival of patients with renal cell carcinoma: Retrospective multi-center study
Siteng Chen, Xiyue Wang, Jun Zhang +8
Background: Clear cell renal cell carcinoma (ccRCC) is the most common renal-related tumor with high heterogeneity. There is still an urgent need for novel diagnostic and prognosti…
DeepNoise: Signal and Noise Disentanglement based on Classifying Fluorescent Microscopy Images via Deep Learning
Sen Yang, Tao Shen, Yuqi Fang +5
The high-content image-based assay is commonly leveraged for identifying the phenotypic impact of genetic perturbations in biology field. However, a persistent issue remains unsolv…
Mitosis domain generalization in histopathology images -- The MIDOG challenge
Marc Aubreville, Nikolas Stathonikos, Christof A. Bertram +32
The density of mitotic figures within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of mitoti…
Deep learning-based approach to reveal tumor mutational burden status from whole slide images across multiple cancer types
Siteng Chen, Jinxi Xiang, Xiyue Wang +6
Tumor mutational burden (TMB) is a potential genomic biomarker of immunotherapy. However, TMB detected through whole exome sequencing lacks clinical penetration in low-resource set…
A Deep Learning Framework for Nuclear Segmentation and Classification in Histopathological Images
Sen Yang, Jinxi Xiang, Xiyue Wang
Nucleus segmentation and classification are the prerequisites in the workflow of digital pathology processing. However, it is very challenging due to its high-level heterogeneity a…
Keep It Accurate and Robust: An Enhanced Nuclei Analysis Framework
Wenhua Zhang, Sen Yang, Meiwei Luo +5
Accurate segmentation and classification of nuclei in histology images is critical but challenging due to nuclei heterogeneity, staining variations, and tissue complexity. Existing…
Sk-Unet Model with Fourier Domain for Mitosis Detection
Sen Yang, Feng Luo, Jun Zhang +1
Mitotic count is the most important morphological feature of breast cancer grading. Many deep learning-based methods have been proposed but suffer from domain shift. In this work,…
Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge
Alain Lalande, Zhihao Chen, Thibaut Pommier +30
A key factor for assessing the state of the heart after myocardial infarction (MI) is to measure whether the myocardium segment is viable after reperfusion or revascularization the…
SK-Unet: an Improved U-net Model with Selective Kernel for the Segmentation of Multi-sequence Cardiac MR
Xiyue Wang, Sen Yang, Mingxuan Tang +4
In the clinical environment, myocardial infarction (MI) as one com-mon cardiovascular disease is mainly evaluated based on the late gadolinium enhancement (LGE) cardiac magnetic re…