469 citations · 1.4k across the 33 of their papers we have counts for
14 papers · 1 filter
BigCell: Generating Gigapixel Whole-Slide Images
Srikar Yellapragada, Alexandros Graikos, Zilinghan Li +10
Whole-slide images (WSIs) are gigapixel-scale digital scans of histopathology tissue and serve as the primary basis for cancer diagnosis. Existing histopathology generative models…
Sharing standardized image-derived data in computational pathology using DICOM
Daniela P. Schacherer, Christopher P. Bridge, David Clunie +13
Development and evaluation of computational pathology methods require access to large and diverse datasets. Over the past decade, various initiatives invested significantly into co…
PathLDM: Text conditioned Latent Diffusion Model for Histopathology
Srikar Yellapragada, Alexandros Graikos, Prateek Prasanna +3
To achieve high-quality results, diffusion models must be trained on large datasets. This can be notably prohibitive for models in specialized domains, such as computational pathol…
Understanding metric-related pitfalls in image analysis validation
Annika Reinke, Minu D. Tizabi, Michael Baumgartner +75
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation int…
Learning Topological Interactions for Multi-Class Medical Image Segmentation
Saumya Gupta, Xiaoling Hu, James Kaan +10
Deep learning methods have achieved impressive performance for multi-class medical image segmentation. However, they are limited in their ability to encode topological interactions…
Metrics reloaded: Recommendations for image analysis validation
Lena Maier-Hein, Annika Reinke, Patrick Godau +71
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen…
Multi-Class Cell Detection Using Spatial Context Representation
Shahira Abousamra, David Belinsky, John Van Arnam +7
In digital pathology, both detection and classification of cells are important for automatic diagnostic and prognostic tasks. Classifying cells into subtypes, such as tumor cells,…
Label Super Resolution with Inter-Instance Loss
Maozheng Zhao, Le Hou, Han Le +8
For the task of semantic segmentation, high-resolution (pixel-level) ground truth is very expensive to collect, especially for high resolution images such as gigapixel pathology im…
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…
Unsupervised Histopathology Image Synthesis
Le Hou, Ayush Agarwal, Dimitris Samaras +3
Hematoxylin and Eosin stained histopathology image analysis is essential for the diagnosis and study of complicated diseases such as cancer. Existing state-of-the-art approaches de…
Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images
Le Hou, Vu Nguyen, Dimitris Samaras +4
Histopathology images are crucial to the study of complex diseases such as cancer. The histologic characteristics of nuclei play a key role in disease diagnosis, prognosis and anal…
Center-Focusing Multi-task CNN with Injected Features for Classification of Glioma Nuclear Images
Veda Murthy, Le Hou, Dimitris Samaras +2
Classifying the various shapes and attributes of a glioma cell nucleus is crucial for diagnosis and understanding the disease. We investigate automated classification of glioma nuc…
Neural Networks with Smooth Adaptive Activation Functions for Regression
Le Hou, Dimitris Samaras, Tahsin M. Kurc +2
In Neural Networks (NN), Adaptive Activation Functions (AAF) have parameters that control the shapes of activation functions. These parameters are trained along with other paramete…
Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification
Le Hou, Dimitris Samaras, Tahsin M. Kurc +3
Convolutional Neural Networks (CNN) are state-of-the-art models for many image classification tasks. However, to recognize cancer subtypes automatically, training a CNN on gigapixe…