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Ensembling Low Precision Models for Binary Biomedical Image Segmentation
Tianyu Ma, Hang Zhang, Hanley Ong +5
Segmentation of anatomical regions of interest such as vessels or small lesions in medical images is still a difficult problem that is often tackled with manual input by an expert.…
Predictive Modeling of Anatomy with Genetic and Clinical Data
Adrian V. Dalca, Ramesh Sridharan, Mert R. Sabuncu +1
We present a semi-parametric generative model for predicting anatomy of a patient in subsequent scans following a single baseline image. Such predictive modeling promises to facili…
Neural encoding with visual attention
Meenakshi Khosla, Gia H. Ngo, Keith Jamison +2
Visual perception is critically influenced by the focus of attention. Due to limited resources, it is well known that neural representations are biased in favor of attended locatio…
Probabilistic Dipole Inversion for Adaptive Quantitative Susceptibility Mapping
Jinwei Zhang, Hang Zhang, Mert Sabuncu +3
A learning-based posterior distribution estimation method, Probabilistic Dipole Inversion (PDI), is proposed to solve the quantitative susceptibility mapping (QSM) inverse problem…
Intelligence plays dice: Stochasticity is essential for machine learning
Mert R. Sabuncu
Many fields view stochasticity as a way to gain computational efficiency, while often having to trade off accuracy. In this perspective article, we argue that stochasticity plays a…
From Connectomic to Task-evoked Fingerprints: Individualized Prediction of Task Contrasts from Resting-state Functional Connectivity
Gia H. Ngo, Meenakshi Khosla, Keith Jamison +2
Resting-state functional MRI (rsfMRI) yields functional connectomes that can serve as cognitive fingerprints of individuals. Connectomic fingerprints have proven useful in many mac…
Neural Network-based Reconstruction in Compressed Sensing MRI Without Fully-sampled Training Data
Alan Q. Wang, Adrian V. Dalca, Mert R. Sabuncu
Compressed Sensing MRI (CS-MRI) has shown promise in reconstructing under-sampled MR images, offering the potential to reduce scan times. Classical techniques minimize a regularize…
Extending LOUPE for K-space Under-sampling Pattern Optimization in Multi-coil MRI
Jinwei Zhang, Hang Zhang, Alan Wang +5
The previously established LOUPE (Learning-based Optimization of the Under-sampling Pattern) framework for optimizing the k-space sampling pattern in MRI was extended in three fold…
A shared neural encoding model for the prediction of subject-specific fMRI response
Meenakshi Khosla, Gia H. Ngo, Keith Jamison +2
The increasing popularity of naturalistic paradigms in fMRI (such as movie watching) demands novel strategies for multi-subject data analysis, such as use of neural encoding models…
Real-Time Uncertainty Estimation in Computer Vision via Uncertainty-Aware Distribution Distillation
Yichen Shen, Zhilu Zhang, Mert R. Sabuncu +1
Calibrated estimates of uncertainty are critical for many real-world computer vision applications of deep learning. While there are several widely-used uncertainty estimation metho…
Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data
Qi Chang, Hui Qu, Yikai Zhang +4
In this paper, we propose a data privacy-preserving and communication efficient distributed GAN learning framework named Distributed Asynchronized Discriminator GAN (AsynDGAN). Our…
Self-Distillation as Instance-Specific Label Smoothing
Zhilu Zhang, Mert R. Sabuncu
It has been recently demonstrated that multi-generational self-distillation can improve generalization. Despite this intriguing observation, reasons for the enhancement remain poor…
An Auto-Encoder Strategy for Adaptive Image Segmentation
Evan M. Yu, Juan Eugenio Iglesias, Adrian V. Dalca +1
Deep neural networks are powerful tools for biomedical image segmentation. These models are often trained with heavy supervision, relying on pairs of images and corresponding voxel…
Bayesian Learning of Probabilistic Dipole Inversion for Quantitative Susceptibility Mapping
Jinwei Zhang, Hang Zhang, Mert Sabuncu +3
A learning-based posterior distribution estimation method, Probabilistic Dipole Inversion (PDI), is proposed to solve quantitative susceptibility mapping (QSM) inverse problem in M…
Volumetric landmark detection with a multi-scale shift equivariant neural network
Tianyu Ma, Ajay Gupta, Mert R. Sabuncu
Deep neural networks yield promising results in a wide range of computer vision applications, including landmark detection. A major challenge for accurate anatomical landmark detec…
RSANet: Recurrent Slice-wise Attention Network for Multiple Sclerosis Lesion Segmentation
Hang Zhang, Jinwei Zhang, Qihao Zhang +7
Brain lesion volume measured on T2 weighted MRI images is a clinically important disease marker in multiple sclerosis (MS). Manual delineation of MS lesions is a time-consuming and…