73 research outputs found
Image-level supervision and self-training for transformer-based cross-modality tumor segmentation
Deep neural networks are commonly used for automated medical image
segmentation, but models will frequently struggle to generalize well across
different imaging modalities. This issue is particularly problematic due to the
limited availability of annotated data, making it difficult to deploy these
models on a larger scale. To overcome these challenges, we propose a new
semi-supervised training strategy called MoDATTS. Our approach is designed for
accurate cross-modality 3D tumor segmentation on unpaired bi-modal datasets. An
image-to-image translation strategy between imaging modalities is used to
produce annotated pseudo-target volumes and improve generalization to the
unannotated target modality. We also use powerful vision transformer
architectures and introduce an iterative self-training procedure to further
close the domain gap between modalities. MoDATTS additionally allows the
possibility to extend the training to unannotated target data by exploiting
image-level labels with an unsupervised objective that encourages the model to
perform 3D diseased-to-healthy translation by disentangling tumors from the
background. The proposed model achieves superior performance compared to other
methods from participating teams in the CrossMoDA 2022 challenge, as evidenced
by its reported top Dice score of 0.87+/-0.04 for the VS segmentation. MoDATTS
also yields consistent improvements in Dice scores over baselines on a
cross-modality brain tumor segmentation task composed of four different
contrasts from the BraTS 2020 challenge dataset, where 95% of a target
supervised model performance is reached. We report that 99% and 100% of this
maximum performance can be attained if 20% and 50% of the target data is
additionally annotated, which further demonstrates that MoDATTS can be
leveraged to reduce the annotation burden.Comment: 17 pages, 10 figures, 5 table
Semi-supervised ViT knowledge distillation network with style transfer normalization for colorectal liver metastases survival prediction
Colorectal liver metastases (CLM) significantly impact colon cancer patients,
influencing survival based on systemic chemotherapy response. Traditional
methods like tumor grading scores (e.g., tumor regression grade - TRG) for
prognosis suffer from subjectivity, time constraints, and expertise demands.
Current machine learning approaches often focus on radiological data, yet the
relevance of histological images for survival predictions, capturing intricate
tumor microenvironment characteristics, is gaining recognition. To address
these limitations, we propose an end-to-end approach for automated prognosis
prediction using histology slides stained with H&E and HPS. We first employ a
Generative Adversarial Network (GAN) for slide normalization to reduce staining
variations and improve the overall quality of the images that are used as input
to our prediction pipeline. We propose a semi-supervised model to perform
tissue classification from sparse annotations, producing feature maps. We use
an attention-based approach that weighs the importance of different slide
regions in producing the final classification results. We exploit the extracted
features for the metastatic nodules and surrounding tissue to train a prognosis
model. In parallel, we train a vision Transformer (ViT) in a knowledge
distillation framework to replicate and enhance the performance of the
prognosis prediction. In our evaluation on a clinical dataset of 258 patients,
our approach demonstrates superior performance with c-indexes of 0.804 (0.014)
for OS and 0.733 (0.014) for TTR. Achieving 86.9% to 90.3% accuracy in
predicting TRG dichotomization and 78.5% to 82.1% accuracy for the 3-class TRG
classification task, our approach outperforms comparative methods. Our proposed
pipeline can provide automated prognosis for pathologists and oncologists, and
can greatly promote precision medicine progress in managing CLM patients.Comment: 16 pages, 7 figures and 7 tables. Submitted to Medical Journal
Analysis (MedIA) journa
Genetic architecture of subcortical brain structures in 38,851 individuals
Subcortical brain structures are integral to motion, consciousness, emotions and learning. We identified common genetic variation related to the volumes of the nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen and thalamus, using genome-wide association analyses in almost 40,000 individuals from CHARGE, ENIGMA and UK Biobank. We show that variability in subcortical volumes is heritable, and identify 48 significantly associated loci (40 novel at the time of analysis). Annotation of these loci by utilizing gene expression, methylation and neuropathological data identified 199 genes putatively implicated in neurodevelopment, synaptic signaling, axonal transport, apoptosis, inflammation/infection and susceptibility to neurological disorders. This set of genes is significantly enriched for Drosophila orthologs associated with neurodevelopmental phenotypes, suggesting evolutionarily conserved mechanisms. Our findings uncover novel biology and potential drug targets underlying brain development and disease
The genetic architecture of the human cerebral cortex
The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder
Genetic architecture of subcortical brain structures in 38,851 individuals
Subcortical brain structures are integral to motion, consciousness, emotions and learning. We identified common genetic variation related to the volumes of the nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen and thalamus, using genome-wide association analyses in almost 40,000 individuals from CHARGE, ENIGMA and UK Biobank. We show that variability in subcortical volumes is heritable, and identify 48 significantly associated loci (40 novel at the time of analysis). Annotation of these loci by utilizing gene expression, methylation and neuropathological data identified 199 genes putatively implicated in neurodevelopment, synaptic signaling, axonal transport, apoptosis, inflammation/infection and susceptibility to neurological disorders. This set of genes is significantly enriched for Drosophila orthologs associated with neurodevelopmental phenotypes, suggesting evolutionarily conserved mechanisms. Our findings uncover novel biology and potential drug targets underlying brain development and disease
The genetic architecture of the human cerebral cortex
The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder
COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study
Background:
The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms.
Methods:
International, prospective observational study of 60â109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms.
Results:
âTypicalâ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (â€â18 years: 69, 48, 23; 85%), older adults (â„â70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each Pâ<â0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country.
Interpretation:
This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men
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