36 citations · 167 across the 20 of their papers we have counts for
20 papers
DMC-Net: Lightweight Dynamic Multi-Scale and Multi-Resolution Convolution Network for Pancreas Segmentation in CT Images
Jin Yang, Daniel S. Marcus, Aristeidis Sotiras
Convolutional neural networks (CNNs) have shown great effectiveness in medical image segmentation. However, they may be limited in modeling large inter-subject variations in organ…
DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image Classification
Wenhui Zhu, Xiwen Chen, Peijie Qiu +3
Multiple instance learning (MIL) stands as a powerful approach in weakly supervised learning, regularly employed in histological whole slide image (WSI) classification for detectin…
SelfReg-UNet: Self-Regularized UNet for Medical Image Segmentation
Wenhui Zhu, Xiwen Chen, Peijie Qiu +4
Since its introduction, UNet has been leading a variety of medical image segmentation tasks. Although numerous follow-up studies have also been dedicated to improving the performan…
TimeMIL: Advancing Multivariate Time Series Classification via a Time-aware Multiple Instance Learning
Xiwen Chen, Peijie Qiu, Wenhui Zhu +5
Deep neural networks, including transformers and convolutional neural networks, have significantly improved multivariate time series classification (MTSC). However, these methods o…
Analyzing heterogeneity in Alzheimer Disease using multimodal normative modeling on imaging-based ATN biomarkers
Sayantan Kumar, Tom Earnest, Braden Yang +10
INTRODUCTION: Previous studies have applied normative modeling on a single neuroimaging modality to investigate Alzheimer Disease (AD) heterogeneity. We employed a deep learning-ba…
AgileFormer: Spatially Agile Transformer UNet for Medical Image Segmentation
Peijie Qiu, Jin Yang, Sayantan Kumar +2
In the past decades, deep neural networks, particularly convolutional neural networks, have achieved state-of-the-art performance in a variety of medical image segmentation tasks.…
Dynamic U-Net: Adaptively Calibrate Features for Abdominal Multi-organ Segmentation
Jin Yang, Daniel S. Marcus, Aristeidis Sotiras
U-Net has been widely used for segmenting abdominal organs, achieving promising performance. However, when it is used for multi-organ segmentation, first, it may be limited in expl…
D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image Segmentation
Jin Yang, Peijie Qiu, Yichi Zhang +2
Hierarchical transformers have achieved significant success in medical image segmentation due to their large receptive field and capabilities of effectively leveraging global long-…
Improving Normative Modeling for Multi-modal Neuroimaging Data using mixture-of-product-of-experts variational autoencoders
Sayantan Kumar, Philip Payne, Aristeidis Sotiras
Normative models in neuroimaging learn the brain patterns of healthy population distribution and estimate how disease subjects like Alzheimer's Disease (AD) deviate from the norm.…
SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classification
Peijie Qiu, Pan Xiao, Wenhui Zhu +2
Multiple Instance Learning (MIL) has been widely used in weakly supervised whole slide image (WSI) classification. Typical MIL methods include a feature embedding part, which embed…
The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI
Ahmed W. Moawad, Anastasia Janas, Ujjwal Baid +229
The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS…
MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network
Satrajit Chakrabarty, Pamela LaMontagne, Joshua Shimony +2
Isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status are important prognostic markers for glioma. Currently, they are determined using invasive procedures. Our goal…
Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-oncology (I3CR-WANO)
Satrajit Chakrabarty, Syed Amaan Abidi, Mina Mousa +12
Efforts to utilize growing volumes of clinical imaging data to generate tumor evaluations continue to require significant manual data wrangling owing to the data heterogeneity. Her…
The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients
Bhakti Baheti, Satrajit Chakrabarty, Hamed Akbari +70
Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been progress in developing gene…
Multidimensional representations in late-life depression: convergence in neuroimaging, cognition, clinical symptomatology and genetics
Junhao Wen, Cynthia H. Y. Fu, Duygu Tosun +22
Late-life depression (LLD) is characterized by considerable heterogeneity in clinical manifestation. Unraveling such heterogeneity would aid in elucidating etiological mechanisms a…
Normative Modeling using Multimodal Variational Autoencoders to Identify Abnormal Brain Structural Patterns in Alzheimer Disease
Sayantan Kumar, Philip Payne, Aristeidis Sotiras
Normative modelling is an emerging method for understanding the underlying heterogeneity within brain disorders like Alzheimer Disease (AD) by quantifying how each patient deviates…
Disentangling Alzheimer's disease neurodegeneration from typical brain aging using machine learning
Gyujoon Hwang, Ahmed Abdulkadir, Guray Erus +18
Neuroimaging biomarkers that distinguish between typical brain aging and Alzheimer's disease (AD) are valuable for determining how much each contributes to cognitive decline. Machi…
MAGIC: Multi-scale Heterogeneity Analysis and Clustering for Brain Diseases
Junhao Wen, Erdem Varol, Ganesh Chand +2
There is a growing amount of clinical, anatomical and functional evidence for the heterogeneous presentation of neuropsychiatric and neurodegenerative diseases such as schizophreni…
The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
Razvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young +93
We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international…
Generative discriminative models for multivariate inference and statistical mapping in medical imaging
Erdem Varol, Aristeidis Sotiras, Ke Zeng +1
This paper presents a general framework for obtaining interpretable multivariate discriminative models that allow efficient statistical inference for neuroimage analysis. The frame…