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Parmida Ghahremani

dblp:247/3995 · DBLP profile ↗
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4ranked-venue papers
3as first author
3since 2021 · last 2023
0000-0002-6935-817XORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 4 · 3 first-author · 3 since 2021Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Human-computer interaction and ubiquitous computing · 1Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
2 papers
Medical and health informatics · 67% Bioinformatics and computational biology · 33%
Computer graphics and multimedia
2 papers
Virtual and augmented reality · 45% Visualization and visual analytics · 34% Rendering · 21%
Human-computer interaction and pervasive computing
1 paper
Immersive interaction · 100%

Topics — the 7 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Medical and health informatics
computational pathology
0.612022
DeepLIIF: An Online Platform for Quantification of Clinical Pathology Slides · CVPR 2022
Medical and health informatics › medical imaging
medical image analysis
0.612022
DeepLIIF: An Online Platform for Quantification of Clinical Pathology Slides · CVPR 2022
Bioinformatics and computational biology › neuroscience
neuroinformatics
0.612022
NeuroConstruct: 3D Reconstruction and Visualization of Neurites in Optical Microscopy Brain Images · IEEE Trans. Vis. Comput. Graph. 2022
Virtual and augmented reality › navigation
immersive navigation
0.412019
Exploration of Large Omnidirectional Images in Immersive Environments · VR 2019
Immersive interaction › virtual reality locomotion
teleportation
0.412019
Exploration of Large Omnidirectional Images in Immersive Environments · VR 2019
Rendering
hybrid rendering
0.212022
NeuroConstruct: 3D Reconstruction and Visualization of Neurites in Optical Microscopy Brain Images · IEEE Trans. Vis. Comput. Graph. 2022
Visualization and visual analytics
volume visualization
0.212022
NeuroConstruct: 3D Reconstruction and Visualization of Neurites in Optical Microscopy Brain Images · IEEE Trans. Vis. Comput. Graph. 2022

Methods — techniques the papers use, named apart from their topics

image registration · 1.1convolutional neural network · 1.1user study · 0.8peek then warp · 0.8auto-scaling · 0.8multiplex immunofluorescence · 0.6isosurface rendering · 0.6iso-surface rendering · 0.6deep learning · 0.6
YearPublicationVenuePosition
2023 An AI-Ready Multiplex Staining Dataset for Reproducible and Accurate Characterization of Tumor Immune Microenvironment
Parmida Ghahremani, Joseph Marino, Juan Hernandez-Prera, Janis V. de la Iglesia, Robbert J. C. Slebos, Christine H. Chung, Saad Nadeem
MICCAI (6)1
2022 DeepLIIF: An Online Platform for Quantification of Clinical Pathology Slides
abstract
In the clinic, resected tissue samples are stained with Hematoxylin-and-Eosin (H&E) and/or Immunhistochemistry (IHC) stains and presented to the pathologists on glass slides or as digital scans for diagnosis and assessment of disease progression. Cell-level quantification, e.g. in IHC protein expression scoring, can be extremely inefficient and subjective. We present DeepLIIF (https://deepliif.org), a first free online platform for efficient and reproducible IHC scoring. DeepLIIF outperforms current state-of-the-art approaches (relying on manual error-prone annotations) by virtually restaining clinical IHC slides with more informative multiplex immunofluorescence staining. Our DeepLIIF cloud-native platform supports (1) more than 150 proprietary/non-proprietary input formats via the Bio-Formats standard, (2) interactive adjustment, visualization, and downloading of the IHC quantification results and the accompanying restained images, (3) consumption of an exposed workflow API programmatically or through interactive plugins for open source whole slide image viewers such as QuPath/ImageJ, and (4) auto scaling to efficiently scale GPU resources based on user demand.
Parmida Ghahremani, Joseph Marino, Ricardo Dodds, Saad Nadeem
CVPR1
2022 NeuroConstruct: 3D Reconstruction and Visualization of Neurites in Optical Microscopy Brain Images
abstract
We introduce NeuroConstruct, a novel end-to-end application for the segmentation, registration, and visualization of brain volumes imaged using wide-field microscopy. NeuroConstruct offers a Segmentation Toolbox with various annotation helper functions that aid experts to effectively and precisely annotate micrometer resolution neurites. It also offers an automatic neurites segmentation using convolutional neuronal networks (CNN) trained by the Toolbox annotations and somas segmentation using thresholding. To visualize neurites in a given volume, NeuroConstruct offers a hybrid rendering by combining iso-surface rendering of high-confidence classified neurites, along with real-time rendering of raw volume using a 2D transfer function for voxel classification score versus voxel intensity value. For a complete reconstruction of the 3D neurites, we introduce a Registration Toolbox that provides automatic coarse-to-fine alignment of serially sectioned samples. The quantitative and qualitative analysis show that NeuroConstruct outperforms the state-of-the-art in all design aspects. NeuroConstruct was developed as a collaboration between computer scientists and neuroscientists, with an application to the study of cholinergic neurons, which are severely affected in Alzheimer's disease.
Parmida Ghahremani, Saeed Boorboor, Pooya Mirhosseini, Chetan Gudisagar, Mala Ananth 0001, David Talmage, Lorna Role, Arie E. Kaufman
IEEE Trans. Vis. Comput. Graph.1
2019 Exploration of Large Omnidirectional Images in Immersive Environments
abstract
Navigation is a major challenge in exploring data within immersive environments, especially of large omnidirectional spherical images. We propose a method of auto-scaling to allow users to navigate using teleportation within the safe boundary of their physical environment with different levels of focus. Our method combines physical navigation with virtual teleportation. We also propose a “peek then warp” behavior when using a zoom lens and evaluate our system in conjunction with different teleportation transitions, including a proposed transition for exploration of omnidirectional and 360-degree panoramic imagery, termed Envelop, wherein the destination view expands out from the zoom lens to completely envelop the user. In this work, we focus on visualizing and navigating large omnidirectional or panoramic images with application to GIS visualization as an inside-out omnidirectional image of the earth. We conducted two user studies to evaluate our techniques over a search and comparison task. Our results illustrate the advantages of our techniques for navigation and exploration of omnidirectional images in an immersive environment.
Seyedkoosha Mirhosseini, Parmida Ghahremani, Sushant Ojal, Joseph Marino, Arie Kaufrnan
VR2