3.9k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of California, BerkeleyUS67 papers
- Stanford UniversityUS37 papers
- University of San FranciscoUS31 papers
- Harvard UniversityUS22 papers
- University of California, Los AngelesUS19 papers
- University of California SystemUS19 papers
- University of PennsylvaniaUS18 papers
- University of WashingtonUS17 papers
- Lawrence Berkeley National LaboratoryUS15 papers
- Johns Hopkins UniversityUS13 papers
- Stanford MedicineUS13 papers
- University of Wisconsin–MadisonUS13 papers
15 papers · 1 filter
ORGAN: Object-Centric Representation Learning using Cycle Consistent Generative Adversarial Networks
Joël Küchler, Ellen van Maren, Vaiva Vasiliauskaitė +3
Although data generation is often straightforward, extracting information from data is more difficult. Object-centric representation learning can extract information from images in…
Shedding Light on the Polymer's Identity: Microplastic Detection and Identification Through Nile Red Staining and Multispectral Imaging (FIMAP)
Derek Ho, Haotian Feng
The widespread distribution of microplastics (MPs) in the environment presents significant challenges for their detection and identification. Fluorescence imaging has emerged as a…
Nellie: Automated organelle segmentation, tracking, and hierarchical feature extraction in 2D/3D live-cell microscopy
Austin E. Y. T. Lefebvre, Gabriel Sturm, Ting-Yu Lin +4
The analysis of dynamic organelles remains a formidable challenge, though key to understanding biological processes. We introduce Nellie, an automated and unbiased user-friendly pi…
Random Expert Sampling for Deep Learning Segmentation of Acute Ischemic Stroke on Non-contrast CT
Sophie Ostmeier, Brian Axelrod, Benjamin Pulli +6
Purpose: Multi-expert deep learning training methods to automatically quantify ischemic brain tissue on Non-Contrast CT Materials and Methods: The data set consisted of 260 Non-Con…
Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging
Todd C. Hollon, Cheng Jiang, Asadur Chowdury +22
Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. However, timely molecular diagnostic t…
Lithium Metal Battery Quality Control via Transformer-CNN Segmentation
Jerome Quenum, Iryna Zenyuk, Daniela Ushizima
Lithium metal battery (LMB) has the potential to be the next-generation battery system because of its high theoretical energy density. However, defects known as dendrites are forme…
Domain-guided data augmentation for deep learning on medical imaging
Chinmayee Athalye, Rima Arnaout
While domain-specific data augmentation can be useful in training neural networks for medical imaging tasks, such techniques have not been widely used to date. Here, we test whethe…
The University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM) Dataset
Evan Calabrese, Javier E. Villanueva-Meyer, Jeffrey D. Rudie +6
Here we present the University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM) dataset. The UCSF-PDGM dataset includes 500 subjects with histopathologically…
Adversarial Policy Gradient for Deep Learning Image Augmentation
Kaiyang Cheng, Claudia Iriondo, Francesco Calivá +3
The use of semantic segmentation for masking and cropping input images has proven to be a significant aid in medical imaging classification tasks by decreasing the noise and varian…
Joint Correction of Attenuation and Scatter Using Deep Convolutional Neural Networks (DCNN) for Time-of-Flight PET
Jaewon Yang, Dookun Park, Jae Ho Sohn +3
Deep convolutional neural networks (DCNN) have demonstrated its capability to convert MR image to pseudo CT for PET attenuation correction in PET/MRI. Conventionally, attenuated ev…
Cost-Sensitive Active Learning for Intracranial Hemorrhage Detection
Weicheng Kuo, Christian Häne, Esther Yuh +2
Deep learning for clinical applications is subject to stringent performance requirements, which raises a need for large labeled datasets. However, the enormous cost of labeling med…
Robust registration of medical images in the presence of spatially-varying noise
Reza Abbasi-Asl, Aboozar Ghaffari, Emad Fatemizadeh
Spatially-varying intensity noise is a common source of distortion in medical images. Bias field noise is one example of such a distortion that is often present in the magnetic res…
Spinal cord gray matter segmentation using deep dilated convolutions
Christian S. Perone, Evan Calabrese, Julien Cohen-Adad
Gray matter (GM) tissue changes have been associated with a wide range of neurological disorders and was also recently found relevant as a biomarker for disability in amyotrophic l…
A Computer Vision Pipeline for Automated Determination of Cardiac Structure and Function and Detection of Disease by Two-Dimensional Echocardiography
Jeffrey Zhang, Sravani Gajjala, Pulkit Agrawal +13
Automated cardiac image interpretation has the potential to transform clinical practice in multiple ways including enabling low-cost serial assessment of cardiac function in the pr…
Structural Compression of Convolutional Neural Networks
Reza Abbasi-Asl, Bin Yu
Deep convolutional neural networks (CNNs) have been successful in many tasks in machine vision, however, millions of weights in the form of thousands of convolutional filters in CN…