703 research outputs found
Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillation
Contrastive learning has shown great promise over annotation scarcity
problems in the context of medical image segmentation. Existing approaches
typically assume a balanced class distribution for both labeled and unlabeled
medical images. However, medical image data in reality is commonly imbalanced
(i.e., multi-class label imbalance), which naturally yields blurry contours and
usually incorrectly labels rare objects. Moreover, it remains unclear whether
all negative samples are equally negative. In this work, we present ACTION, an
Anatomical-aware ConTrastive dIstillatiON framework, for semi-supervised
medical image segmentation. Specifically, we first develop an iterative
contrastive distillation algorithm by softly labeling the negatives rather than
binary supervision between positive and negative pairs. We also capture more
semantically similar features from the randomly chosen negative set compared to
the positives to enforce the diversity of the sampled data. Second, we raise a
more important question: Can we really handle imbalanced samples to yield
better performance? Hence, the key innovation in ACTION is to learn global
semantic relationship across the entire dataset and local anatomical features
among the neighbouring pixels with minimal additional memory footprint. During
the training, we introduce anatomical contrast by actively sampling a sparse
set of hard negative pixels, which can generate smoother segmentation
boundaries and more accurate predictions. Extensive experiments across two
benchmark datasets and different unlabeled settings show that ACTION
significantly outperforms the current state-of-the-art semi-supervised methods.Comment: Accepted at Information Processing in Medical Imaging (IPMI 2023
Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts
Integrating high-level semantically correlated contents and low-level
anatomical features is of central importance in medical image segmentation.
Towards this end, recent deep learning-based medical segmentation methods have
shown great promise in better modeling such information. However, convolution
operators for medical segmentation typically operate on regular grids, which
inherently blur the high-frequency regions, i.e., boundary regions. In this
work, we propose MORSE, a generic implicit neural rendering framework designed
at an anatomical level to assist learning in medical image segmentation. Our
method is motivated by the fact that implicit neural representation has been
shown to be more effective in fitting complex signals and solving computer
graphics problems than discrete grid-based representation. The core of our
approach is to formulate medical image segmentation as a rendering problem in
an end-to-end manner. Specifically, we continuously align the coarse
segmentation prediction with the ambiguous coordinate-based point
representations and aggregate these features to adaptively refine the boundary
region. To parallelly optimize multi-scale pixel-level features, we leverage
the idea from Mixture-of-Expert (MoE) to design and train our MORSE with a
stochastic gating mechanism. Our experiments demonstrate that MORSE can work
well with different medical segmentation backbones, consistently achieving
competitive performance improvements in both 2D and 3D supervised medical
segmentation methods. We also theoretically analyze the superiority of MORSE.Comment: Accepted at International Conference on Medical Image Computing and
Computer-Assisted Intervention (MICCAI 2023
ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast
Medical data often exhibits long-tail distributions with heavy class
imbalance, which naturally leads to difficulty in classifying the minority
classes (i.e., boundary regions or rare objects). Recent work has significantly
improved semi-supervised medical image segmentation in long-tailed scenarios by
equipping them with unsupervised contrastive criteria. However, it remains
unclear how well they will perform in the labeled portion of data where class
distribution is also highly imbalanced. In this work, we present ACTION++, an
improved contrastive learning framework with adaptive anatomical contrast for
semi-supervised medical segmentation. Specifically, we propose an adaptive
supervised contrastive loss, where we first compute the optimal locations of
class centers uniformly distributed on the embedding space (i.e., off-line),
and then perform online contrastive matching training by encouraging different
class features to adaptively match these distinct and uniformly distributed
class centers. Moreover, we argue that blindly adopting a constant temperature
in the contrastive loss on long-tailed medical data is not optimal, and
propose to use a dynamic via a simple cosine schedule to yield better
separation between majority and minority classes. Empirically, we evaluate
ACTION++ on ACDC and LA benchmarks and show that it achieves state-of-the-art
across two semi-supervised settings. Theoretically, we analyze the performance
of adaptive anatomical contrast and confirm its superiority in label
efficiency.Comment: Accepted by International Conference on Medical Image Computing and
Computer-Assisted Intervention (MICCAI 2023
Phosphodiesterase-5 inhibitors in management of pulmonary hypertension: safety, tolerability, and efficacy
Pulmonary arterial hypertension (PAH) is a progressive disease that causes severe disability and has no cure. Over the past 20 years, a variety of treatment options have evolved for the management of PAH. With an expanded therapeutic armamentarium come more complex decisions regarding treatment options. Agent selection depends upon several factors including efficacy, side effect profile, and cost, as well as convenience of administration. We have undertaken a review of phosphodiesterase-5 (PDE-5) inhibitors in PAH with a focus on efficacy and safety. A literature search was conducted using the Medline and Cochrane Central Register of Controlled Trials databases (1966–February 2010) for relevant randomized clinical studies. Overall, 10 studies met our inclusion criteria. Sildenafil was the most commonly studied agent, followed by tadalafil and vardenafil. Most trials found that the PDE-5 inhibitors significantly improved exercise capacity and lowered pulmonary pressures. However, there were conflicting results regarding these agents’ impact on improving cardiac function and functional class. Overall, these medications were effective and well tolerated with a relatively benign side effect profile. The PDE-5 inhibitors are an important option in treating PAH. While most of the published clinical data involved sildenafil, the other PDE-5 inhibitors show promise as well. Further studies are needed to determine the optimal doses of this therapeutic drug class, as well as its effects as adjunctive therapy with other agents in PAH
Recommended from our members
Using Perturbation Theory to Compute the Morphological Similarity of Diffusion Tensors
Computing the morphological similarity of diffusion tensors (DTs) at neighboring voxels within a DT image, or at corresponding locations across different DT images, is a fundamental and ubiquitous operation in the postprocessing of DT images. The morphological similarity of DTs typically has been computed using either the principal directions (PDs) of DTs (i.e., the direction along which water molecules diffuse preferentially) or their tensor elements. Although comparing PDs allows the similarity of one morphological feature of DTs to be visualized directly in eigenspace, this method takes into account only a single eigenvector, and it is therefore sensitive to the presence of noise in the images that can introduce error in to the estimation of that vector. Although comparing tensor elements, rather than PDs, is comparatively more robust to the effects of noise, the individual elements of a given tensor do not directly reflect the diffusion properties of water molecules. We propose a measure for computing the morphological similarity of DTs that uses both their eigenvalues and eigenvectors, and that also accounts for the noise levels present in DT images. Our measure presupposes that DTs in a homogeneous region within or across DT images are random perturbations of one another in the presence of noise. The similarity values that are computed using our method are smooth (in the sense that small changes in eigenvalues and eigenvectors cause only small changes in similarity), and they are symmetric when differences in eigenvalues and eigenvectors are also symmetric. In addition, our method does not presuppose that the corresponding eigenvectors across two DTs have been identified accurately, an assumption that is problematic in the presence of noise. Because we compute the similarity between DTs using their eigenspace components, our similarity measure relates directly to both the magnitude and the direction of the diffusion of water molecules. The favorable performance characteristics of our measure offer the prospect of substantially improving additional postprocessing operations that are commonly performed on DTI datasets, such as image segmentation, fiber tracking, noise filtering, and spatial normalization
Phonological Features for 0-shot Multilingual Speech Synthesis
Code-switching---the intra-utterance use of multiple languages---is prevalent
across the world. Within text-to-speech (TTS), multilingual models have been
found to enable code-switching. By modifying the linguistic input to
sequence-to-sequence TTS, we show that code-switching is possible for languages
unseen during training, even within monolingual models. We use a small set of
phonological features derived from the International Phonetic Alphabet (IPA),
such as vowel height and frontness, consonant place and manner. This allows the
model topology to stay unchanged for different languages, and enables new,
previously unseen feature combinations to be interpreted by the model. We show
that this allows us to generate intelligible, code-switched speech in a new
language at test time, including the approximation of sounds never seen in
training.Comment: 5 pages, to be presented at INTERSPEECH 202
Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation
Many medical datasets have recently been created for medical image
segmentation tasks, and it is natural to question whether we can use them to
sequentially train a single model that (1) performs better on all these
datasets, and (2) generalizes well and transfers better to the unknown target
site domain. Prior works have achieved this goal by jointly training one model
on multi-site datasets, which achieve competitive performance on average but
such methods rely on the assumption about the availability of all training
data, thus limiting its effectiveness in practical deployment. In this paper,
we propose a novel multi-site segmentation framework called
incremental-transfer learning (ITL), which learns a model from multi-site
datasets in an end-to-end sequential fashion. Specifically, "incremental"
refers to training sequentially constructed datasets, and "transfer" is
achieved by leveraging useful information from the linear combination of
embedding features on each dataset. In addition, we introduce our ITL
framework, where we train the network including a site-agnostic encoder with
pre-trained weights and at most two segmentation decoder heads. We also design
a novel site-level incremental loss in order to generalize well on the target
domain. Second, we show for the first time that leveraging our ITL training
scheme is able to alleviate challenging catastrophic forgetting problems in
incremental learning. We conduct experiments using five challenging benchmark
datasets to validate the effectiveness of our incremental-transfer learning
approach. Our approach makes minimal assumptions on computation resources and
domain-specific expertise, and hence constitutes a strong starting point in
multi-site medical image segmentation
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective
For medical image segmentation, contrastive learning is the dominant practice
to improve the quality of visual representations by contrasting semantically
similar and dissimilar pairs of samples. This is enabled by the observation
that without accessing ground truth label, negative examples with truly
dissimilar anatomical features, if sampled, can significantly improve the
performance. In reality, however, these samples may come from similar
anatomical features and the models may struggle to distinguish the minority
tail-class samples, making the tail classes more prone to misclassification,
both of which typically lead to model collapse. In this paper, we propose ARCO,
a semi-supervised contrastive learning (CL) framework with stratified group
sampling theory in medical image segmentation. In particular, we first propose
building ARCO through the concept of variance-reduced estimation, and show that
certain variance-reduction techniques are particularly beneficial in medical
image segmentation tasks with extremely limited labels. Furthermore, we
theoretically prove these sampling techniques are universal in variance
reduction. Finally, we experimentally validate our approaches on three
benchmark datasets with different label settings, and our methods consistently
outperform state-of-the-art semi-supervised methods. Additionally, we augment
the CL frameworks with these sampling techniques and demonstrate significant
gains over previous methods. We believe our work is an important step towards
semi-supervised medical image segmentation by quantifying the limitation of
current self-supervision objectives for accomplishing medical image analysis
tasks
- …