38 research outputs found
Coarse-to-fine Knowledge Graph Domain Adaptation based on Distantly-supervised Iterative Training
Modern supervised learning neural network models require a large amount of
manually labeled data, which makes the construction of domain-specific
knowledge graphs time-consuming and labor-intensive. In parallel, although
there has been much research on named entity recognition and relation
extraction based on distantly supervised learning, constructing a
domain-specific knowledge graph from large collections of textual data without
manual annotations is still an urgent problem to be solved. In response, we
propose an integrated framework for adapting and re-learning knowledge graphs
from one coarse domain (biomedical) to a finer-define domain (oncology). In
this framework, we apply distant-supervision on cross-domain knowledge graph
adaptation. Consequently, no manual data annotation is required to train the
model. We introduce a novel iterative training strategy to facilitate the
discovery of domain-specific named entities and triples. Experimental results
indicate that the proposed framework can perform domain adaptation and
construction of knowledge graph efficiently
Segment Anything Model (SAM) for Radiation Oncology
In this study, we evaluate the performance of the Segment Anything Model
(SAM) model in clinical radiotherapy. We collected real clinical cases from
four regions at the Mayo Clinic: prostate, lung, gastrointestinal, and head \&
neck, which are typical treatment sites in radiation oncology. For each case,
we selected the OARs of concern in radiotherapy planning and compared the Dice
and Jaccard outcomes between clinical manual delineation, automatic
segmentation using SAM's "segment anything" mode, and automatic segmentation
using SAM with box prompt. Our results indicate that SAM performs better in
automatic segmentation for the prostate and lung regions, while its performance
in the gastrointestinal and head \& neck regions was relatively inferior. When
considering the size of the organ and the clarity of its boundary, SAM displays
better performance for larger organs with clear boundaries, such as the lung
and liver, and worse for smaller organs with unclear boundaries, like the
parotid and cochlea. These findings align with the generally accepted
variations in difficulty level associated with manual delineation of different
organs at different sites in clinical radiotherapy. Given that SAM, a single
trained model, could handle the delineation of OARs in four regions, these
results also demonstrate SAM's robust generalization capabilities in automatic
segmentation for radiotherapy, i.e., achieving delineation of different
radiotherapy OARs using a generic automatic segmentation model. SAM's
generalization capabilities across different regions make it technically
feasible to develop a generic model for automatic segmentation in radiotherapy
Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference
Alzheimer's disease (AD) is a neurodegenerative disorder that is beginning
with amyloidosis, followed by neuronal loss and deterioration in structure,
function, and cognition. The accumulation of amyloid-beta in the brain,
measured through 18F-florbetapir (AV45) positron emission tomography (PET)
imaging, has been widely used for early diagnosis of AD. However, the
relationship between amyloid-beta accumulation and AD pathophysiology remains
unclear, and causal inference approaches are needed to uncover how amyloid-beta
levels can impact AD development. In this paper, we propose a graph varying
coefficient neural network (GVCNet) for estimating the individual treatment
effect with continuous treatment levels using a graph convolutional neural
network. We highlight the potential of causal inference approaches, including
GVCNet, for measuring the regional causal connections between amyloid-beta
accumulation and AD pathophysiology, which may serve as a robust tool for early
diagnosis and tailored care