Jinan Charafeddine

@article{shakir_3063,

title = {Novel Approximate Distribution of the Generalized Turbulence Channels for MIMO FSO Communications},

author = {Wafaa Shakir and Ali Mahdi and Hani Hamdan and Jinan Charafeddine and Haitham Satai and Radouane Akrache and Samir Hadad and Jinane Sayah},

url = {https://ieeexplore.ieee.org/document/10568928},

year  = {2024},

date = {2024-08-01},

journal = {Ieee Photonics Journal},

volume = {16},

number = {4},

pages = {1 - 15},

abstract = {In this paper, we develop an innovative series representation for the sum of Rician non-zero boresight pointing error random variates based on the k - ? distribution, which is suitable for multiple-input multiple-output (MIMO) transmission for the first time. Then, using this new representation, we introduce a novel closed-form probability density function (PDF) approximation for the sum of Gamma-Gamma random variates with generalized pointing errors and atmospheric attenuation of MIMO free-space optical (FSO) communications. Statistical Kolmogorov-Smirnov tests confirm the accuracy of this approximation over a wide range of channel conditions. The significance of this approximation is emphasized by deriving closed-form expressions for the ergodic capacity, outage probability, and average bit error rate (BER) using Meijer's G-function. This paper provides a comprehensive analysis of the performance of MIMO FSO systems utilizing the equal gain combining (EGC) diversity technique under various conditions, such as different numbers of transmitter and receiver, turbulence intensities, the effects of non-zero boresight pointing errors, and path attenuation. The results show that using MIMO technology with more transmitters and receivers significantly improves the performance of FSO communication compared to other diversity techniques, including single input single output (SISO), and multiple input single output (MISO) systems. Detailed evaluations of the ergodic capacity, outage probability, and average BER performance at high signal-to-noise ratios provide additional insights. Monte-Carlo simulation results demonstrate the accuracy of the proposed approach.},

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}

@article{dornaika_2943,

title = {Towards unsupervised radiograph clustering for COVID-19: The use of graph-based multi-view clustering},

author = {Fadi Dornaika and Sally El Hajjar and Jinan Charafeddine},

url = {https://www.sciencedirect.com/science/article/pii/S0952197624004949?via%3Dihub},

year  = {2024},

date = {2024-07-01},

journal = {Engineering Applications Of Artificial Intelligence},

volume = {133},

number = {Part D},

pages = {108336},

abstract = {Automatic classification methods widely used for diagnosing and analyzing COVID-19 cases. These methods assume known labels and rely on a single view of the dataset. Given the prevalence of COVID-19 cases and the extensive volume of patient records lacking labels, this communication underscores our unique approach?conducting the first study on COVID-19 case diagnosis in an unsupervised manner. Our work operates under the assumption of prior knowledge regarding the number of classes, such as COVID-19, pneumonia, and normal, in a case study.



By adopting an unsupervised learning paradigm, we leverage the wealth of unlabeled data, reducing dependence on human experts for annotating numerous images. This paper introduces an enhanced version of a recent direct method where non-negative cluster indices and spectral embeddings are jointly estimated. Beyond the inherent advantages of this method, our proposed model introduces improvements through two additional types of constraints: (i) ensuring consistent smoothing of cluster labels across all views and (ii) imposing an orthogonality constraint on the matrix of cluster assignments. The efficacy of the proposed method is demonstrated using the public COVIDx dataset with three classes, showcasing promising results in categorizing radiographs. The proposed approach is tested on other public image datasets to assess its effectiveness.},

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pubstate = {published},

tppubtype = {article}

}

@article{sun_3024,

title = {LCAMix: Local-and-contour aware grid mixing based data augmentation for medical image segmentation},

author = {Danyang Sun and Fadi Dornaika and Jinan Charafeddine},

url = {https://pdf.sciencedirectassets.com/272144/1-s2.0-S1566253524X00068/1-s2.0-S1566253524002628/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEBcaCXVzLWVhc3QtMSJHMEUCIB%2Bm2yVgKP%2BTa1w1tSg%2FydrF%2Fvb0NtGUi2OtE0dCfm5ZAiEAvS5zznXIlJGGqU0uDk6urwemR%2FjBXI8flOEVsv%2Bqi7MqvAUIj%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FARAFGgwwNTkwMDM1NDY4NjUiDGlNeMR%2BKpLBIx%2FJkiqQBUxi37rUay01aPHdPyUjlEmZScIOm09oNOKMm2j4kAJ%2BBXu4xZ6ts%2FYVWeKloZ9bKO688mVLpZYW96XCoW8DS9E%2FGvhCoLNFgFEV35VB1ai8O7M21%2BlU%2Br8%2FcPbj77ypLNC%2BKDWJV0NPrB1DKOvao6ZpbspbrdK4kPspEAzZ%2FVkFT%2FKiopgMXf6iniq2tANtPaizXkk%2BZaNSAGIFt9OmCXMk6H7VYJALKEF4ZJm9kBslRj5nE5Y6edtx2JSSptvlujKMXhJbwjIiUiX7Ui1B4pUWDVtzmLpVcElBM78VwS0H8TKlHXBl5pyVDCym%2Fp2r%2BZbZdJWW6ZWgrqb26Dqc%2F22RSCtts8Lm%2Fs8vSHEc2cfI2mYpPSj5gL51yaDqoZyA9JcEBsQRAecvv%2F8RkbvtzMzuNBRjn4V8HoQR137Q2pYM%2FJgAly5tkgk5KS8JB2jb%2BaTA88ZJLx6YSDeqJdX2PXwyB1XvrrRy8oUgMmff8s2zVgT1f1dX5iPmZ1jtYEVu40cdvJ88ac8j4zAfWQPmBb%2FvfXWd%2BDVOp7h8ftVwQnI7bLDlFBXbhH3oVb%2FFpJ3Bw3DFakM7j%2FSOVug7DP8dC9h8Dj2oIOW8C4QAgdCeHaDRMfh2F7VZjw3FZf3i631%2BAdBp8r4d4XkYeREmbmiovEoVWBBHoYD6RTIK9lp37AmDnhFSAMr2LEmzG2NypUQ3Hwb7zLu4thOxdGz5MBApBAh44ukY8YuN1ufa38eyKRtCUO%2Fxd2kuIIJ2LVXa3bQYug5Lx%2B9KIKhgqxCrwptvf8OaxtQ5FoYhuqNU0pL1Chohou2d1vld8RViOconvLl0VWMsZ5YyP9ZxaM0xtzdz9jZQUUyYOV92u8ehS7ie1qwrMNDAwrIGOrEBxpUgPvpVhdMKearFfabKLjWh5dtniMHIFTE5cSfjDp3cUuAHaEMlV2m22S1E7%2B%2BqLhJRHUxrx0Ahj9AgijOS6Ej7rGG3xGTKGOnhYGjvrjhMzhpYH7bBR2UDVY%2Ff5JnU3IqbM8%2BmzPkwAtPDE4f74N3zJ3CUErPd4nF0UOXyQABK0JE3XGOZmeqIIDshevKKQuAQWWCmCYyGaPWgjtPkrFNgDEqhkhXN4QWxs%2FJfC2Tb&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20240524T152432Z&X-Amz-SignedHeaders=host&X-Amz-Expires=299&X-Amz-Credential=ASIAQ3PHCVTY4RJ4ZEGT%2F20240524%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=49fff6e8e4aa2160d63ba58a605230103a570a99a899c33049a2447724567bf6&hash=cf866cff1fcd93c461170f4149fdcc586b3e8c5bd13fbef0361db16227b04cc0&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S1566253524002628&tid=spdf-53f47d50-21b1-4fa1-a93b-92addbddb736&sid=5b8e3010830f534aec5849b2797ace9af2f9gxrqb&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=001156565a0352015207&rr=888e4b0cfc530203&cc=fr},

year  = {2024},

date = {2024-05-01},

journal = {Information Fusion},

volume = {110},

pages = {102484},

abstract = {Medical image segmentation often faces challenges related to overfitting, primarily due to the limited and complex training samples. This challenge often prompts the use of self-supervised learning and data augmentation. However, self-supervised learning requires well-defined hand-crafted tasks and multiple training stages. On the other hand, basic image augmentation techniques like cropping, rotation, and flipping, effective for natural scene images, have limited efficacy for medical images due to their isotropic nature.



While regional dropout regularization data augmentation methods have proven effective in image recognition tasks, their application in image segmentation is not as extensively studied. Additionally, existing augmentation methods often operate on square regions, leading to the loss of crucial contour information. This is particularly problematic for medical image segmentation tasks dealing with regions of interest characterized by intricate shapes. In this work, we introduce LCAMix, a novel data augmentation approach designed for medical image segmentation. LCAMix operates by blending two images and their segmentation masks based on their superpixels, incorporating a local-and-contour-aware strategy. The training process on augmented images adopts two auxiliary pretext tasks: firstly, classifying local superpixels in augmented images using an adaptive focal margin, leveraging segmentation ground truth masks as prior knowledge; secondly, reconstructing the two source images using mixed superpixels as mutual masks, emphasizing spatial sensitivity. Our method stands out as a simple, one-stage, model-agnostic, and plug-and-play data augmentation solution applicable to various segmentation tasks. Notably, it requires no external data or additional models. Extensive experiments validate its superior performance across diverse medical segmentation datasets and tasks. The source codes are available at https://github.com/DanielaPlusPlus/DataAug4Medical.},

keywords = {},

pubstate = {online},

tppubtype = {article}

}