16 research outputs found
Improve Robustness of Eye Disease Detection by including Learnable Probabilistic Discrete Latent Variables into Machine Learning Models
Ocular diseases, ranging from diabetic retinopathy to glaucoma, present a
significant public health challenge due to their prevalence and potential for
causing vision impairment. Early and accurate diagnosis is crucial for
effective treatment and management.In recent years, deep learning models have
emerged as powerful tools for analysing medical images, including ocular
imaging . However, challenges persist in model interpretability and uncertainty
estimation, which are critical for clinical decision-making. This study
introduces a novel application of GFlowOut, leveraging the probabilistic
framework of Generative Flow Networks (GFlowNets) to learn the posterior
distribution over dropout masks, for the classification and analysis of ocular
diseases using eye fundus images. We develop a robust and generalizable method
that utilizes GFlowOut integrated with ResNet18 and ViT models as backbone in
identifying various ocular conditions. This study employs a unique set of
dropout masks - none, random, bottomup, and topdown - to enhance model
performance in analyzing ocular images. Our results demonstrate that the
bottomup GFlowOut mask significantly improves accuracy, outperforming the
traditional dropout approach.Comment: This is a work in progres
A New Dataset and Method for Creativity Assessment Using the Alternate Uses Task
Creativity ratings by humans for the alternate uses task (AUT) tend to be subjective and inefficient. To automate the scoring process of the AUT, previous literature suggested using semantic distance from non-contextual models. In this paper, we extend this line of research by including contextual semantic models and more importantly, exploring the feasibility of predicting creativity ratings with supervised discriminative machine learning models. Based on a newly collected dataset, our results show that supervised models can successfully classify between creative and non-creative responses even with unbalanced data, and can generalise well to out-of-domain unseen prompts
Performance Optimization for Federated Person Re-identification via Benchmark Analysis
Federated learning is a privacy-preserving machine learning technique that
learns a shared model across decentralized clients. It can alleviate privacy
concerns of personal re-identification, an important computer vision task. In
this work, we implement federated learning to person re-identification
(FedReID) and optimize its performance affected by statistical heterogeneity in
the real-world scenario. We first construct a new benchmark to investigate the
performance of FedReID. This benchmark consists of (1) nine datasets with
different volumes sourced from different domains to simulate the heterogeneous
situation in reality, (2) two federated scenarios, and (3) an enhanced
federated algorithm for FedReID. The benchmark analysis shows that the
client-edge-cloud architecture, represented by the federated-by-dataset
scenario, has better performance than client-server architecture in FedReID. It
also reveals the bottlenecks of FedReID under the real-world scenario,
including poor performance of large datasets caused by unbalanced weights in
model aggregation and challenges in convergence. Then we propose two
optimization methods: (1) To address the unbalanced weight problem, we propose
a new method to dynamically change the weights according to the scale of model
changes in clients in each training round; (2) To facilitate convergence, we
adopt knowledge distillation to refine the server model with knowledge
generated from client models on a public dataset. Experiment results
demonstrate that our strategies can achieve much better convergence with
superior performance on all datasets. We believe that our work will inspire the
community to further explore the implementation of federated learning on more
computer vision tasks in real-world scenarios.Comment: ACMMM'2
Harvard Eye Fairness: A Large-Scale 3D Imaging Dataset for Equitable Eye Diseases Screening and Fair Identity Scaling
Fairness or equity in machine learning is profoundly important for societal
well-being, but limited public datasets hinder its progress, especially in the
area of medicine. It is undeniable that fairness in medicine is one of the most
important areas for fairness learning's applications. Currently, no large-scale
public medical datasets with 3D imaging data for fairness learning are
available, while 3D imaging data in modern clinics are standard tests for
disease diagnosis. In addition, existing medical fairness datasets are actually
repurposed datasets, and therefore they typically have limited demographic
identity attributes with at most three identity attributes of age, gender, and
race for fairness modeling. To address this gap, we introduce our Eye Fairness
dataset with 30,000 subjects (Harvard-EF) covering three major eye diseases
including age-related macular degeneration, diabetic retinopathy, and glaucoma
affecting 380 million patients globally. Our Harvard-EF dataset includes both
2D fundus photos and 3D optical coherence tomography scans with six demographic
identity attributes including age, gender, race, ethnicity, preferred language,
and marital status. We also propose a fair identity scaling (FIS) approach
combining group and individual scaling together to improve model fairness. Our
FIS approach is compared with various state-of-the-art fairness learning
methods with superior performance in the racial, gender, and ethnicity fairness
tasks with 2D and 3D imaging data, which demonstrate the utilities of our
Harvard-EF dataset for fairness learning. To facilitate fairness comparisons
between different models, we propose performance-scaled disparity measures,
which can be used to compare model fairness accounting for overall performance
levels. The dataset and code are publicly accessible via
https://ophai.hms.harvard.edu/datasets/harvard-ef30k
A Comprehensive Survey on Database Management System Fuzzing: Techniques, Taxonomy and Experimental Comparison
Database Management System (DBMS) fuzzing is an automated testing technique
aimed at detecting errors and vulnerabilities in DBMSs by generating, mutating,
and executing test cases. It not only reduces the time and cost of manual
testing but also enhances detection coverage, providing valuable assistance in
developing commercial DBMSs. Existing fuzzing surveys mainly focus on
general-purpose software. However, DBMSs are different from them in terms of
internal structure, input/output, and test objectives, requiring specialized
fuzzing strategies. Therefore, this paper focuses on DBMS fuzzing and provides
a comprehensive review and comparison of the methods in this field. We first
introduce the fundamental concepts. Then, we systematically define a general
fuzzing procedure and decompose and categorize existing methods. Furthermore,
we classify existing methods from the testing objective perspective, covering
various components in DBMSs. For representative works, more detailed
descriptions are provided to analyze their strengths and limitations. To
objectively evaluate the performance of each method, we present an open-source
DBMS fuzzing toolkit, OpenDBFuzz. Based on this toolkit, we conduct a detailed
experimental comparative analysis of existing methods and finally discuss
future research directions.Comment: 34 pages, 22 figure