68 citations · 76 across the 14 of their papers we have counts for
17 papers
SplitFed-CL: A Split Federated Co-Learning Framework for Medical Image Segmentation with Inaccurate Labels
Zahra Hafezi Kafshgari, Hadi Hadizadeh, Parvaneh Saeedi
Split Federated Learning (SplitFed) combines federated and split learning to preserve privacy while reducing client-side computation. However, in medical image segmentation, hetero…
MuCALD-SplitFed: Causal-Latent Diffusion for Privacy-Preserving Multi-Task Split-Federated Medical Image Segmentation
Chamani Shiranthika, Hadi Hadizadeh, Parvaneh Saeedi
Federated Learning enables decentralized training by aggregating model updates across clients without sharing raw data, while Split Federated Learning further partitions the model…
When To Adapt? Adapting the Model or Data in Federated Medical Imaging
Chamani Shiranthika, Parvaneh Saeedi
Federated learning enables collaborative model training across medical institutions without sharing raw data, but its performance is often limited by domain heterogeneity across cl…
Smart Split-Federated Learning over Noisy Channels for Embryo Image Segmentation
Zahra Hafezi Kafshgari, Ivan V. Bajic, Parvaneh Saeedi
Split-Federated (SplitFed) learning is an extension of federated learning that places minimal requirements on the clients computing infrastructure, since only a small portion of th…
MedSegNet10: A Publicly Accessible Network Repository for Split Federated Medical Image Segmentation
Chamani Shiranthika, Zahra Hafezi Kafshgari, Hadi Hadizadeh +1
Machine Learning (ML) and Deep Learning (DL) have shown significant promise in healthcare, particularly in medical image segmentation, which is crucial for accurate disease diagnos…
SplitFedZip: Learned Compression for Data Transfer Reduction in Split-Federated Learning
Chamani Shiranthika, Hadi Hadizadeh, Parvaneh Saeedi +1
Federated Learning (FL) enables multiple clients to train a collaborative model without sharing their local data. Split Learning (SL) allows a model to be trained in a split manner…
Reinforcement Learning for Unsupervised Video Summarization with Reward Generator Training
Mehryar Abbasi, Hadi Hadizadeh, Parvaneh Saeedi
This paper presents a novel approach for unsupervised video summarization using reinforcement learning (RL), addressing limitations like unstable adversarial training and reliance…
Optimizing Split Points for Error-Resilient SplitFed Learning
Chamani Shiranthika, Parvaneh Saeedi, Ivan V. Bajić
Recent advancements in decentralized learning, such as Federated Learning (FL), Split Learning (SL), and Split Federated Learning (SplitFed), have expanded the potentials of machin…
SplitFed resilience to packet loss: Where to split, that is the question
Chamani Shiranthika, Zahra Hafezi Kafshgari, Parvaneh Saeedi +1
Decentralized machine learning has broadened its scope recently with the invention of Federated Learning (FL), Split Learning (SL), and their hybrids like Split Federated Learning…
Quality-Adaptive Split-Federated Learning for Segmenting Medical Images with Inaccurate Annotations
Zahra Hafezi Kafshgari, Chamani Shiranthika, Parvaneh Saeedi +1
SplitFed Learning, a combination of Federated and Split Learning (FL and SL), is one of the most recent developments in the decentralized machine learning domain. In SplitFed learn…
Enhancing Multivariate Time Series Classifiers through Self-Attention and Relative Positioning Infusion
Mehryar Abbasi, Parvaneh Saeedi
Time Series Classification (TSC) is an important and challenging task for many visual computing applications. Despite the extensive range of methods developed for TSC, relatively f…
Illumination-Invariant Image from 4-Channel Images: The Effect of Near-Infrared Data in Shadow Removal
Sorour Mohajerani, Mark S. Drew, Parvaneh Saeedi
Removing the effect of illumination variation in images has been proved to be beneficial in many computer vision applications such as object recognition and semantic segmentation.…
Cloud and Cloud Shadow Segmentation for Remote Sensing Imagery via Filtered Jaccard Loss Function and Parametric Augmentation
Sorour Mohajerani, Parvaneh Saeedi
Cloud and cloud shadow segmentation are fundamental processes in optical remote sensing image analysis. Current methods for cloud/shadow identification in geospatial imagery are no…
Preimplantation Blastomere Boundary Identification in HMC Microscopic Images of Early Stage Human Embryos
Shakiba Kheradmand, Parvaneh Saeedi, Jason Au +1
We present a novel method for identification of the boundary of embryonic cells (blastomeres) in Hoffman Modulation Contrast (HMC) microscopic images that are taken between day one…
Cloud-Net: An end-to-end Cloud Detection Algorithm for Landsat 8 Imagery
Sorour Mohajerani, Parvaneh Saeedi
Cloud detection in satellite images is an important first-step in many remote sensing applications. This problem is more challenging when only a limited number of spectral bands ar…
Cloud Detection Algorithm for Remote Sensing Images Using Fully Convolutional Neural Networks
Sorour Mohajerani, Thomas A. Krammer, Parvaneh Saeedi
This paper presents a deep-learning based framework for addressing the problem of accurate cloud detection in remote sensing images. This framework benefits from a Fully Convolutio…
CPNet: A Context Preserver Convolutional Neural Network for Detecting Shadows in Single RGB Images
Sorour Mohajerani, Parvaneh Saeedi
Automatic detection of shadow regions in an image is a difficult task due to the lack of prior information about the illumination source and the dynamic of the scene objects. To ad…