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20182026
most citedVirchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

52 citations · 159 across the 15 of their papers we have counts for

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Showing eess.IVShow all

5 papers · 1 filter

eess.IV2024★ 18 cited

PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathology

George Shaikovski, Adam Casson, Kristen Severson +19

Foundation models in computational pathology promise to unlock the development of new clinical decision support systems and models for precision medicine. However, there is a misma…

eess.IV2024

RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models

Farhad Maleki, Linda Moy, Reza Forghani +14

Deep learning techniques hold immense promise for advancing medical image analysis, particularly in tasks like image segmentation, where precise annotation of regions or volumes of…

eess.IV2022

Best Practices and Scoring System on Reviewing A.I. based Medical Imaging Papers: Part 1 Classification

Timothy L. Kline, Felipe Kitamura, Ian Pan +12

With the recent advances in A.I. methodologies and their application to medical imaging, there has been an explosion of related research programs utilizing these techniques to prod…

eess.IV2019★ 9 cited

DeepAAA: clinically applicable and generalizable detection of abdominal aortic aneurysm using deep learning

Jen-Tang Lu, Rupert Brooks, Stefan Hahn +9

We propose a deep learning-based technique for detection and quantification of abdominal aortic aneurysms (AAAs). The condition, which leads to more than 10,000 deaths per year in…

eess.IV2019

4D CNN for semantic segmentation of cardiac volumetric sequences

Andriy Myronenko, Dong Yang, Varun Buch +6

We propose a 4D convolutional neural network (CNN) for the segmentation of retrospective ECG-gated cardiac CT, a series of single-channel volumetric data over time. While only a sm…