267 research outputs found
Statistical Methods for Analyzing Rare Variant Complex Trait Associations via Sequence Data
There is solid evidence that complex human diseases can be caused by rare variants. Next generation sequencing technology has revolutionized the study of complex human diseases, and made possible detecting associations with rare variants. Traditional statistical methods can be inefficient for analyzing sequence data and underpowered. In addition, due to high cost of sequencing, it is also necessary to explore novel cost effective studies in order to maximize power and reduce sequencing cost. In this thesis, three important problems for analyzing sequence data and detecting associations with rare variants are presented. In the first chapter, we presented a new method for detecting rare variants/binary trait associations in the presence of gene interactions. In the second chapter, we explored cost effective study designs for replicating sequence based association studies, combining both sequencing and customized genotyping. In the third chapter, we present a method for analyzing multiple phenotypes in selected samples, such that phenotypes that are commonly measured in different studies can be jointly analyzed to improve power. The methods and study designs presented are important for dissecting complex trait etiologies using sequence data
Robust Core-Periphery Constrained Transformer for Domain Adaptation
Unsupervised domain adaptation (UDA) aims to learn transferable
representation across domains. Recently a few UDA works have successfully
applied Transformer-based methods and achieved state-of-the-art (SOTA) results.
However, it remains challenging when there exists a large domain gap between
the source and target domain. Inspired by humans' exceptional transferability
abilities to adapt knowledge from familiar to uncharted domains, we try to
apply the universally existing organizational structure in the human functional
brain networks, i.e., the core-periphery principle to design the Transformer
and improve its UDA performance. In this paper, we propose a novel
brain-inspired robust core-periphery constrained transformer (RCCT) for
unsupervised domain adaptation, which brings a large margin of performance
improvement on various datasets. Specifically, in RCCT, the self-attention
operation across image patches is rescheduled by an adaptively learned weighted
graph with the Core-Periphery structure (CP graph), where the information
communication and exchange between images patches are manipulated and
controlled by the connection strength, i.e., edge weight of the learned
weighted CP graph. Besides, since the data in domain adaptation tasks can be
noisy, to improve the model robustness, we intentionally add perturbations to
the patches in the latent space to ensure generating robust learned weighted
core-periphery graphs. Extensive evaluations are conducted on several widely
tested UDA benchmarks. Our proposed RCCT consistently performs best compared to
existing works, including 88.3\% on Office-Home, 95.0\% on Office-31, 90.7\% on
VisDA-2017, and 46.0\% on DomainNet.Comment: Core-Periphery, ViT, Unsupervised domain adaptatio
Comparison of the Weibull and the Crow-AMSAA Model in Prediction of Early Cable Joint Failures
Exploring the Influence of Information Entropy Change in Learning Systems
In this work, we explore the influence of entropy change in deep learning
systems by adding noise to the inputs/latent features. The applications in this
paper focus on deep learning tasks within computer vision, but the proposed
theory can be further applied to other fields. Noise is conventionally viewed
as a harmful perturbation in various deep learning architectures, such as
convolutional neural networks (CNNs) and vision transformers (ViTs), as well as
different learning tasks like image classification and transfer learning.
However, this paper aims to rethink whether the conventional proposition always
holds. We demonstrate that specific noise can boost the performance of various
deep architectures under certain conditions. We theoretically prove the
enhancement gained from positive noise by reducing the task complexity defined
by information entropy and experimentally show the significant performance gain
in large image datasets, such as the ImageNet. Herein, we use the information
entropy to define the complexity of the task. We categorize the noise into two
types, positive noise (PN) and harmful noise (HN), based on whether the noise
can help reduce the complexity of the task. Extensive experiments of CNNs and
ViTs have shown performance improvements by proactively injecting positive
noise, where we achieved an unprecedented top 1 accuracy of over 95% on
ImageNet. Both theoretical analysis and empirical evidence have confirmed that
the presence of positive noise can benefit the learning process, while the
traditionally perceived harmful noise indeed impairs deep learning models. The
different roles of noise offer new explanations for deep models on specific
tasks and provide a new paradigm for improving model performance. Moreover, it
reminds us that we can influence the performance of learning systems via
information entropy change.Comment: Information Entropy, CNN, Transforme
Core-Periphery Principle Guided Redesign of Self-Attention in Transformers
Designing more efficient, reliable, and explainable neural network
architectures is critical to studies that are based on artificial intelligence
(AI) techniques. Previous studies, by post-hoc analysis, have found that the
best-performing ANNs surprisingly resemble biological neural networks (BNN),
which indicates that ANNs and BNNs may share some common principles to achieve
optimal performance in either machine learning or cognitive/behavior tasks.
Inspired by this phenomenon, we proactively instill organizational principles
of BNNs to guide the redesign of ANNs. We leverage the Core-Periphery (CP)
organization, which is widely found in human brain networks, to guide the
information communication mechanism in the self-attention of vision transformer
(ViT) and name this novel framework as CP-ViT. In CP-ViT, the attention
operation between nodes is defined by a sparse graph with a Core-Periphery
structure (CP graph), where the core nodes are redesigned and reorganized to
play an integrative role and serve as a center for other periphery nodes to
exchange information. We evaluated the proposed CP-ViT on multiple public
datasets, including medical image datasets (INbreast) and natural image
datasets. Interestingly, by incorporating the BNN-derived principle (CP
structure) into the redesign of ViT, our CP-ViT outperforms other
state-of-the-art ANNs. In general, our work advances the state of the art in
three aspects: 1) This work provides novel insights for brain-inspired AI: we
can utilize the principles found in BNNs to guide and improve our ANN
architecture design; 2) We show that there exist sweet spots of CP graphs that
lead to CP-ViTs with significantly improved performance; and 3) The core nodes
in CP-ViT correspond to task-related meaningful and important image patches,
which can significantly enhance the interpretability of the trained deep model.Comment: Core-periphery, functional brain networks, Vi
Hierarchical Semantic Tree Concept Whitening for Interpretable Image Classification
With the popularity of deep neural networks (DNNs), model interpretability is
becoming a critical concern. Many approaches have been developed to tackle the
problem through post-hoc analysis, such as explaining how predictions are made
or understanding the meaning of neurons in middle layers. Nevertheless, these
methods can only discover the patterns or rules that naturally exist in models.
In this work, rather than relying on post-hoc schemes, we proactively instill
knowledge to alter the representation of human-understandable concepts in
hidden layers. Specifically, we use a hierarchical tree of semantic concepts to
store the knowledge, which is leveraged to regularize the representations of
image data instances while training deep models. The axes of the latent space
are aligned with the semantic concepts, where the hierarchical relations
between concepts are also preserved. Experiments on real-world image datasets
show that our method improves model interpretability, showing better
disentanglement of semantic concepts, without negatively affecting model
classification performance
Artificial General Intelligence for Medical Imaging
In this review, we explore the potential applications of Artificial General
Intelligence (AGI) models in healthcare, focusing on foundational Large
Language Models (LLMs), Large Vision Models, and Large Multimodal Models. We
emphasize the importance of integrating clinical expertise, domain knowledge,
and multimodal capabilities into AGI models. In addition, we lay out key
roadmaps that guide the development and deployment of healthcare AGI models.
Throughout the review, we provide critical perspectives on the potential
challenges and pitfalls associated with deploying large-scale AGI models in the
medical field. This comprehensive review aims to offer insights into the future
implications of AGI in medical imaging, healthcare and beyond
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
RadOnc-GPT: A Large Language Model for Radiation Oncology
This paper presents RadOnc-GPT, a large language model specialized for
radiation oncology through advanced tuning methods. RadOnc-GPT was finetuned on
a large dataset of radiation oncology patient records and clinical notes from
the Mayo Clinic in Arizona. The model employs instruction tuning on three key
tasks - generating radiotherapy treatment regimens, determining optimal
radiation modalities, and providing diagnostic descriptions/ICD codes based on
patient diagnostic details. Evaluations conducted by comparing RadOnc-GPT
outputs to general large language model outputs showed that RadOnc-GPT
generated outputs with significantly improved clarity, specificity, and
clinical relevance. The study demonstrated the potential of using large
language models fine-tuned using domain-specific knowledge like RadOnc-GPT to
achieve transformational capabilities in highly specialized healthcare fields
such as radiation oncology
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