29 research outputs found
Empirical Estimation of Intra-Voxel Structure with Persistent Angular Structure and Q-ball Models of Diffusion Weighted MRI
Fair Federated Medical Image Segmentation via Client Contribution Estimation
How to ensure fairness is an important topic in federated learning (FL).
Recent studies have investigated how to reward clients based on their
contribution (collaboration fairness), and how to achieve uniformity of
performance across clients (performance fairness). Despite achieving progress
on either one, we argue that it is critical to consider them together, in order
to engage and motivate more diverse clients joining FL to derive a high-quality
global model. In this work, we propose a novel method to optimize both types of
fairness simultaneously. Specifically, we propose to estimate client
contribution in gradient and data space. In gradient space, we monitor the
gradient direction differences of each client with respect to others. And in
data space, we measure the prediction error on client data using an auxiliary
model. Based on this contribution estimation, we propose a FL method, federated
training via contribution estimation (FedCE), i.e., using estimation as global
model aggregation weights. We have theoretically analyzed our method and
empirically evaluated it on two real-world medical datasets. The effectiveness
of our approach has been validated with significant performance improvements,
better collaboration fairness, better performance fairness, and comprehensive
analytical studies.Comment: Accepted at CVPR 202
COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image Segmentation
Medical image segmentation is a critical task in medical image analysis. In
recent years, deep learning based approaches have shown exceptional performance
when trained on a fully-annotated dataset. However, data annotation is often a
significant bottleneck, especially for 3D medical images. Active learning (AL)
is a promising solution for efficient annotation but requires an initial set of
labeled samples to start active selection. When the entire data pool is
unlabeled, how do we select the samples to annotate as our initial set? This is
also known as the cold-start AL, which permits only one chance to request
annotations from experts without access to previously annotated data.
Cold-start AL is highly relevant in many practical scenarios but has been
under-explored, especially for 3D medical segmentation tasks requiring
substantial annotation effort. In this paper, we present a benchmark named
COLosSAL by evaluating six cold-start AL strategies on five 3D medical image
segmentation tasks from the public Medical Segmentation Decathlon collection.
We perform a thorough performance analysis and explore important open questions
for cold-start AL, such as the impact of budget on different strategies. Our
results show that cold-start AL is still an unsolved problem for 3D
segmentation tasks but some important trends have been observed. The code
repository, data partitions, and baseline results for the complete benchmark
are publicly available at https://github.com/MedICL-VU/COLosSAL.Comment: Accepted by MICCAI 202
A Unified Single-stage Learning Model for Estimating Fiber Orientation Distribution Functions on Heterogeneous Multi-shell Diffusion-weighted MRI
Diffusion-weighted (DW) MRI measures the direction and scale of the local
diffusion process in every voxel through its spectrum in q-space, typically
acquired in one or more shells. Recent developments in micro-structure imaging
and multi-tissue decomposition have sparked renewed attention to the radial
b-value dependence of the signal. Applications in tissue classification and
micro-architecture estimation, therefore, require a signal representation that
extends over the radial as well as angular domain. Multiple approaches have
been proposed that can model the non-linear relationship between the DW-MRI
signal and biological microstructure. In the past few years, many deep
learning-based methods have been developed towards faster inference speed and
higher inter-scan consistency compared with traditional model-based methods
(e.g., multi-shell multi-tissue constrained spherical deconvolution). However,
a multi-stage learning strategy is typically required since the learning
process relied on various middle representations, such as simple harmonic
oscillator reconstruction (SHORE) representation. In this work, we present a
unified dynamic network with a single-stage spherical convolutional neural
network, which allows efficient fiber orientation distribution function (fODF)
estimation through heterogeneous multi-shell diffusion MRI sequences. We study
the Human Connectome Project (HCP) young adults with test-retest scans. From
the experimental results, the proposed single-stage method outperforms prior
multi-stage approaches in repeated fODF estimation with shell dropoff and
single-shell DW-MRI sequences
Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training
Harnessing the power of pre-training on large-scale datasets like ImageNet
forms a fundamental building block for the progress of representation
learning-driven solutions in computer vision. Medical images are inherently
different from natural images as they are acquired in the form of many
modalities (CT, MR, PET, Ultrasound etc.) and contain granulated information
like tissue, lesion, organs etc. These characteristics of medical images
require special attention towards learning features representative of local
context. In this work, we focus on designing an effective pre-training
framework for 3D radiology images. First, we propose a new masking strategy
called local masking where the masking is performed across channel embeddings
instead of tokens to improve the learning of local feature representations. We
combine this with classical low-level perturbations like adding noise and
downsampling to further enable low-level representation learning. To this end,
we introduce Disruptive Autoencoders, a pre-training framework that attempts to
reconstruct the original image from disruptions created by a combination of
local masking and low-level perturbations. Additionally, we also devise a
cross-modal contrastive loss (CMCL) to accommodate the pre-training of multiple
modalities in a single framework. We curate a large-scale dataset to enable
pre-training of 3D medical radiology images (MRI and CT). The proposed
pre-training framework is tested across multiple downstream tasks and achieves
state-of-the-art performance. Notably, our proposed method tops the public test
leaderboard of BTCV multi-organ segmentation challenge.Comment: Preprin