Publications (40)
A Probabilistic Hadamard U-Net for MRI Bias Field Correction
Xin Zhu, Hongyi Pan, Yury Velichko +5
Magnetic field inhomogeneity correction remains a challenging task in MRI analysis. Most established techniques are designed for brain MRI by supposing that image intensities in th…
A novel asymmetrical autoencoder with a sparsifying discrete cosine Stockwell transform layer for gearbox sensor data compression
Xin Zhu, Daoguang Yang, Hongyi Pan +3
The lack of an efficient compression model remains a challenge for the wireless transmission of gearbox data in non-contact gear fault diagnosis problems. In this paper, we present…
MDNet: Multi-Decoder Network for Abdominal CT Organs Segmentation
Debesh Jha, Nikhil Kumar Tomar, Koushik Biswas +11
Accurate segmentation of organs from abdominal CT scans is essential for clinical applications such as diagnosis, treatment planning, and patient monitoring. To handle challenges o…
Detecting Anomaly in Chemical Sensors via L1-Kernels based Principal Component Analysis
Hongyi Pan, Diaa Badawi, Ishaan Bassi +2
We propose a kernel-PCA based method to detect anomaly in chemical sensors. We use temporal signals produced by chemical sensors to form vectors to perform the Principal Component…
BreastMammo and DenseMammo: Benchmarks for Mammography Domain Generalization
Hongyi Pan, Gorkem Durak, Halil Ertugrul Aktas +18
Breast density classification is a critical component of breast cancer risk assessment, yet AI models often struggle to generalize across clinical sites due to vendor-specific acqu…
IPMN Risk Assessment under Federated Learning Paradigm
Hongyi Pan, Ziliang Hong, Gorkem Durak +17
Accurate classification of Intraductal Papillary Mucinous Neoplasms (IPMN) is essential for identifying high-risk cases that require timely intervention. In this study, we develop…
Classroom Simulacra: Building Contextual Student Generative Agents in Online Education for Learning Behavioral Simulation
Songlin Xu, Hao-Ning Wen, Hongyi Pan +3
Student simulation supports educators to improve teaching by interacting with virtual students. However, most existing approaches ignore the modulation effects of course materials…
Wildfire Detection Via Transfer Learning: A Survey
Ziliang Hong, Emadeldeen Hamdan, Yifei Zhao +3
This paper surveys different publicly available neural network models used for detecting wildfires using regular visible-range cameras which are placed on hilltops or forest lookou…
Cyst-X: A Multi-Center MRI Benchmark and Federated Learning Framework for Malignancy-Risk Stratification of Pancreatic Cystic Neoplasm
Hongyi Pan, Gorkem Durak, Elif Keles +27
Pancreatic cancer is projected to be the second-deadliest cancer by 2030, making early detection critical. Intraductal papillary mucinous neoplasms (IPMNs), key cancer precursors,…
LUMINA: A Multi-Vendor Mammography Benchmark with Energy Harmonization Protocol
Hongyi Pan, Gorkem Durak, Halil Ertugrul Aktas +9
Publicly available full-field digital mammography (FFDM) datasets remain limited in size, clinical annotations, and vendor diversity, hindering the development of robust models. We…
Classification of the Cervical Vertebrae Maturation (CVM) stages Using the Tripod Network
Salih Atici, Hongyi Pan, Mohammed H. Elnagar +4
We present a novel deep learning method for fully automated detection and classification of the Cervical Vertebrae Maturation (CVM) stages. The deep convolutional neural network co…
Frequency-Based Federated Domain Generalization for Polyp Segmentation
Hongyi Pan, Debesh Jha, Koushik Biswas +1
Federated Learning (FL) offers a powerful strategy for training machine learning models across decentralized datasets while maintaining data privacy, yet domain shifts among client…
Sparse Mamba: Introducing Controllability, Observability, And Stability To Structural State Space Models
Emadeldeen Hamdan, Hongyi Pan, Ahmet Enis Cetin
Structured state space models' (SSMs) development in recent studies, such as Mamba and Mamba2, outperformed and solved the computational inefficiency of transformers and large lang…
MammoClean: Toward Reproducible and Bias-Aware AI in Mammography through Dataset Harmonization
Yalda Zafari, Hongyi Pan, Gorkem Durak +3
The development of clinically reliable artificial intelligence (AI) systems for mammography is hindered by profound heterogeneity in data quality, metadata standards, and populatio…
Real-time Wireless ECG-derived Respiration Rate Estimation Using an Autoencoder with a DCT Layer
Hongyi Pan, Xin Zhu, Zhilu Ye +2
In this paper, we present a wireless ECG-derived Respiration Rate (RR) estimation using an autoencoder with a DCT Layer. The wireless wearable system records the ECG data of the su…
DCT Perceptron Layer: A Transform Domain Approach for Convolution Layer
Hongyi Pan, Xin Zhu, Salih Atici +1
In this paper, we propose a novel Discrete Cosine Transform (DCT)-based neural network layer which we call DCT-perceptron to replace the Conv2D layers in the Residual ne…
Multiplication-Avoiding Variant of Power Iteration with Applications
Hongyi Pan, Diaa Badawi, Runxuan Miao +2
Power iteration is a fundamental algorithm in data analysis. It extracts the eigenvector corresponding to the largest eigenvalue of a given matrix. Applications include ranking alg…
GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classification
Bin Wang, Hongyi Pan, Armstrong Aboah +7
Eye tracking research is important in computer vision because it can help us understand how humans interact with the visual world. Specifically for high-risk applications, such as…
ADC/DAC-Free Analog Acceleration of Deep Neural Networks with Frequency Transformation
Nastaran Darabi, Maeesha Binte Hashem, Hongyi Pan +3
The edge processing of deep neural networks (DNNs) is becoming increasingly important due to its ability to extract valuable information directly at the data source to minimize lat…
Pancreas Part Segmentation under Federated Learning Paradigm
Ziliang Hong, Halil Ertugrul Aktas, Andrea Mia Bejar +15
We present the first federated learning (FL) approach for pancreas part(head, body and tail) segmentation in MRI, addressing a critical clinical challenge as a significant innovati…
PAM-UNet: Shifting Attention on Region of Interest in Medical Images
Abhijit Das, Debesh Jha, Vandan Gorade +7
Computer-aided segmentation methods can assist medical personnel in improving diagnostic outcomes. While recent advancements like UNet and its variants have shown promise, they fac…
Domain Generalization with Fourier Transform and Soft Thresholding
Hongyi Pan, Bin Wang, Zheyuan Zhang +5
Domain generalization aims to train models on multiple source domains so that they can generalize well to unseen target domains. Among many domain generalization methods, Fourier-t…
Multipod Convolutional Network
Hongyi Pan, Salih Atici, Ahmet Enis Cetin
In this paper, we introduce a convolutional network which we call MultiPodNet consisting of a combination of two or more convolutional networks which process the input image in par…
Edge-Fog Computing-Enabled EEG Data Compression via Asymmetrical Variational Discrete Cosine Transform Network
Xin Zhu, Hongyi Pan, Ahmet Enis Cetin
The large volume of electroencephalograph (EEG) data produced by brain-computer interface (BCI) systems presents challenges for rapid transmission over bandwidth-limited channels i…
VHU-Net: Variational Hadamard U-Net for Body MRI Bias Field Correction
Xin Zhu, Ahmet Enis Cetin, Gorkem Durak +13
Bias field artifacts in magnetic resonance imaging (MRI) scans introduce spatially smooth intensity inhomogeneities that degrade image quality and hinder downstream analysis. To ad…
Large-Scale Multi-Center CT and MRI Segmentation of Pancreas with Deep Learning
Zheyuan Zhang, Elif Keles, Gorkem Durak +35
Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is…
Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers
Hongyi Pan, Emadeldeen Hamdan, Xin Zhu +2
Self-attention is central to the success of Transformer architectures; however, learning the query, key, and value projections from random initialization remains challenging and co…
Input Normalized Stochastic Gradient Descent Training of Deep Neural Networks
Salih Atici, Hongyi Pan, Ahmet Enis Cetin
In this paper, we propose a novel optimization algorithm for training machine learning models called Input Normalized Stochastic Gradient Descent (INSGD), inspired by the Normalize…
VideoAds for Fast-Paced Video Understanding
Zheyuan Zhang, Monica Dou, Linkai Peng +3
Advertisement videos serve as a rich and valuable source of purpose-driven information, encompassing high-quality visual, textual, and contextual cues designed to engage viewers. T…
Stein Variational Gradient Descent-based Detection For Random Access With Preambles In MTC
Xin Zhu, Hongyi Pan, Salih Atici +1
Traditional preamble detection algorithms have low accuracy in the grant-based random access scheme in massive machine-type communication (mMTC). We present a novel preamble detect…
PolypDB: A Curated Multi-Center Dataset for Development of AI Algorithms in Colonoscopy
Debesh Jha, Nikhil Kumar Tomar, Vanshali Sharma +19
Colonoscopy is the primary method for examination, detection, and removal of polyps. However, challenges such as variations among the endoscopists' skills, bowel quality preparatio…
Robust Principal Component Analysis Using a Novel Kernel Related with the L1-Norm
Hongyi Pan, Diaa Badawi, Erdem Koyuncu +1
We consider a family of vector dot products that can be implemented using sign changes and addition operations only. The dot products are energy-efficient as they avoid the multipl…
Electroencephalogram Sensor Data Compression Using An Asymmetrical Sparse Autoencoder With A Discrete Cosine Transform Layer
Xin Zhu, Hongyi Pan, Shuaiang Rong +1
Electroencephalogram (EEG) data compression is necessary for wireless recording applications to reduce the amount of data that needs to be transmitted. In this paper, an asymmetric…
Fast Walsh-Hadamard Transform and Smooth-Thresholding Based Binary Layers in Deep Neural Networks
Hongyi Pan, Diaa Dabawi, Ahmet Enis Cetin
In this paper, we propose a novel layer based on fast Walsh-Hadamard transform (WHT) and smooth-thresholding to replace convolution layers in deep neural networks. In t…
Ethical Framework for Responsible Foundational Models in Medical Imaging
Abhijit Das, Gorkem Durak, Debesh Jha +34
The emergence of foundational models represents a paradigm shift in medical imaging, offering extraordinary capabilities in disease detection, diagnosis, and treatment planning. Th…
Multichannel Orthogonal Transform-Based Perceptron Layers for Efficient ResNets
Hongyi Pan, Emadeldeen Hamdan, Xin Zhu +2
In this paper, we propose a set of transform-based neural network layers as an alternative to the Conv2D layers in Convolutional Neural Networks (CNNs). The proposed lay…
Block Walsh-Hadamard Transform Based Binary Layers in Deep Neural Networks
Hongyi Pan, Diaa Badawi, Ahmet Enis Cetin
Convolution has been the core operation of modern deep neural networks. It is well-known that convolutions can be implemented in the Fourier Transform domain. In this paper, we pro…
Adaptive Aggregation Weights for Federated Segmentation of Pancreas MRI
Hongyi Pan, Gorkem Durak, Zheyuan Zhang +16
Federated learning (FL) enables collaborative model training across institutions without sharing sensitive data, making it an attractive solution for medical imaging tasks. However…
Federated Breast Cancer Detection Enhanced by Synthetic Ultrasound Image Augmentation
Hongyi Pan, Ziliang Hong, Gorkem Durak +2
Federated learning enables collaborative training of deep learning models across institutions without sharing sensitive patient data. However, its performance is often limited by s…
A Hybrid Quantum-Classical Approach based on the Hadamard Transform for the Convolutional Layer
Hongyi Pan, Xin Zhu, Salih Atici +1
In this paper, we propose a novel Hadamard Transform (HT)-based neural network layer for hybrid quantum-classical computing. It implements the regular convolutional layers in the H…