Search Results for author:
Found 18 papers, 10 papers with code
A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge.
Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
The FeTA Challenge 2022 was able to successfully evaluate and advance generalizability of multi-class fetal brain tissue segmentation algorithms for MRI and it continues to benchmark new algorithms.
Ordinal Classification with Distance Regularization for Robust Brain Age Prediction
However, these methods are subject to an inherent regression to the mean effect, which causes a systematic bias resulting in an overestimation of brain age in young subjects and underestimation in old subjects.
Ranked #1 on
Ordinal Classification
on OASIS+NACC+ICBM+ABIDE+IXI
Domain-knowledge Inspired Pseudo Supervision (DIPS) for Unsupervised Image-to-Image Translation Models to Support Cross-Domain Classification
Cross-domain classification frameworks were developed to handle this data domain shift problem by utilizing unsupervised image-to-image translation models to translate an input image from the unlabeled domain to the labeled domain.
Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR Images
Harnessing the power of deep neural networks in the medical imaging domain is challenging due to the difficulties in acquiring large annotated datasets, especially for rare diseases, which involve high costs, time, and effort for annotation.
Ranked #1 on
Anomaly Detection
on ADNI
The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge
It is intended to resolve the above-mentioned problems and promote the development of both intracranial hemorrhage segmentation and anisotropic data processing.
A Framework for Generalizing Critical Heat Flux Detection Models Using Unsupervised Image-to-Image Translation
To deal with datasets from new domains a model needs to be trained from scratch.
Automated head and neck tumor segmentation from 3D PET/CT
Head and neck tumor segmentation challenge (HECKTOR) 2022 offers a platform for researchers to compare their solutions to segmentation of tumors and lymph nodes from 3D CT and PET images.
Automated segmentation of intracranial hemorrhages from 3D CT
Intracranial hemorrhage segmentation challenge (INSTANCE 2022) offers a platform for researchers to compare their solutions to segmentation of hemorrhage stroke regions from 3D CTs.
HealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human Disease
Therefore, this paper poses the research question of how to improve unsupervised anomaly detection by utilizing (1) an unannotated set of mixed images, in addition to (2) the set of healthy images as being used in the literature.
Fetal Brain Tissue Annotation and Segmentation Challenge Results
Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context.
HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet
Given the recent success of deep learning in medical image segmentation, Neural Architecture Search (NAS) has been introduced to find high-performance 3D segmentation network architectures.
Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs
Another year of the multimodal brain tumor segmentation challenge (BraTS) 2021 provides an even larger dataset to facilitate collaboration and research of brain tumor segmentation methods, which are necessary for disease analysis and treatment planning.
A2B-GAN: Utilizing Unannotated Anomalous Images for Anomaly Detection in Medical Image Analysis
This paper poses the research question of how to improve anomaly detection by using an unannotated set of mixed images of both normal and anomalous samples (in addition to a set of normal images from healthy subjects).
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN).
Ranked #1 on
Medical Image Segmentation
on EM
(IoU metric)
Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis
More importantly, learning a model from scratch simply in 3D may not necessarily yield performance better than transfer learning from ImageNet in 2D, but our Models Genesis consistently top any 2D approaches including fine-tuning the models pre-trained from ImageNet as well as fine-tuning the 2D versions of our Models Genesis, confirming the importance of 3D anatomical information and significance of our Models Genesis for 3D medical imaging.
Ranked #1 on
Pulmonary Embolism Detection
on PE-CAD FPRED
Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization
Qualitative and quantitative evaluations demonstrate that the proposed method outperforms the state of the art in multi-domain image-to-image translation and that it surpasses predominant weakly-supervised localization methods in both disease detection and localization.
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet
Ranked #1 on
Video Polyp Segmentation
on SUN-SEG-Easy
