40 research outputs found
PDL: Regularizing Multiple Instance Learning with Progressive Dropout Layers
Multiple instance learning (MIL) was a weakly supervised learning approach
that sought to assign binary class labels to collections of instances known as
bags. However, due to their weak supervision nature, the MIL methods were
susceptible to overfitting and required assistance in developing comprehensive
representations of target instances. While regularization typically effectively
combated overfitting, its integration with the MIL model has been frequently
overlooked in prior studies. Meanwhile, current regularization methods for MIL
have shown limitations in their capacity to uncover a diverse array of
representations. In this study, we delve into the realm of regularization
within the MIL model, presenting a novel approach in the form of a Progressive
Dropout Layer (PDL). We aim to not only address overfitting but also empower
the MIL model in uncovering intricate and impactful feature representations.
The proposed method was orthogonal to existing MIL methods and could be easily
integrated into them to boost performance. Our extensive evaluation across a
range of MIL benchmark datasets demonstrated that the incorporation of the PDL
into multiple MIL methods not only elevated their classification performance
but also augmented their potential for weakly-supervised feature localizations.Comment: The code is available in https://github.com/ChongQingNoSubway/PD
NNMobile-Net: Rethinking CNN Design for Deep Learning-Based Retinopathy Research
Retinal diseases (RD) are the leading cause of severe vision loss or
blindness. Deep learning-based automated tools play an indispensable role in
assisting clinicians in diagnosing and monitoring RD in modern medicine.
Recently, an increasing number of works in this field have taken advantage of
Vision Transformer to achieve state-of-the-art performance with more parameters
and higher model complexity compared to Convolutional Neural Networks (CNNs).
Such sophisticated and task-specific model designs, however, are prone to be
overfitting and hinder their generalizability. In this work, we argue that a
channel-aware and well-calibrated CNN model may overcome these problems. To
this end, we empirically studied CNN's macro and micro designs and its training
strategies. Based on the investigation, we proposed a no-new-MobleNet
(nn-MobileNet) developed for retinal diseases. In our experiments, our generic,
simple and efficient model superseded most current state-of-the-art methods on
four public datasets for multiple tasks, including diabetic retinopathy
grading, fundus multi-disease detection, and diabetic macular edema
classification. Our work may provide novel insights into deep learning
architecture design and advance retinopathy research.Comment: Code will publish soon:
https://github.com/Retinal-Research/NNMOBILE-NE
OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing
Non-mydriatic retinal color fundus photography (CFP) is widely available due
to the advantage of not requiring pupillary dilation, however, is prone to poor
quality due to operators, systemic imperfections, or patient-related causes.
Optimal retinal image quality is mandated for accurate medical diagnoses and
automated analyses. Herein, we leveraged the Optimal Transport (OT) theory to
propose an unpaired image-to-image translation scheme for mapping low-quality
retinal CFPs to high-quality counterparts. Furthermore, to improve the
flexibility, robustness, and applicability of our image enhancement pipeline in
the clinical practice, we generalized a state-of-the-art model-based image
reconstruction method, regularization by denoising, by plugging in priors
learned by our OT-guided image-to-image translation network. We named it as
regularization by enhancing (RE). We validated the integrated framework, OTRE,
on three publicly available retinal image datasets by assessing the quality
after enhancement and their performance on various downstream tasks, including
diabetic retinopathy grading, vessel segmentation, and diabetic lesion
segmentation. The experimental results demonstrated the superiority of our
proposed framework over some state-of-the-art unsupervised competitors and a
state-of-the-art supervised method.Comment: Accepted as a conference paper to The 28th biennial international
conference on Information Processing in Medical Imaging (IPMI 2023
Prediction of intracranial hemorrhagic events based on retinal microvascular abnormalities: a meta-analysis
Retinal microvascular abnormalities have been shown to be associated with intracranial hemorrhage (ICH) in several studies. The standardization of the retinal findings and the degree of the association remain unclear. Objective: To synthesize estimates of risk across cohort studies and to quantify the association of retinal microvascular signs with incident intracranial bleeding events
Understanding Optic Neuritis from Murine Models of Multiple Sclerosis
The availability of a good animal model is critical for understanding MS and developing therapies to control the disease. The primary experimental MS animal model has been the experimental autoimmune encephalomyelitis (EAE) model. Another model of viral induced CNS demyelination was recently published, based on ocular infection with a recombinant HSV-1 constitutively expressing murine IL-2 (HSV-IL-2)