Mani Abedini, PhD.

Mani Abedini, PhD.

دبي الإمارات العربية المتحدة
٢ ألف متابع أكثر من 500 زميل

نبذة عني

Steering data science and AI initives, my leadership pivots on innovating with Generative…

الخبرة

  • رسم بياني AW Rostamani Group

    AW Rostamani Group

    Dubai, United Arab Emirates

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    United Arab Emirates

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    Dubai, United Arab Emirates

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    Melbourne, Victoria, Australia

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    Melbourne, Victoria, Australia

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    Melbourne, Australia

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    Melbourne, Australia

التعليم

التراخيص والشهادات

المنشورات

  • Skin Cancer Classification by Leveraging Segment Anything Model for Semantic Segmentation of Skin Lesion

    International Journal of Advanced Research in Computer and Communication Engineering

    Skin cancer is a growing public health concern; while some types of skin cancer are deadly, such as Melanoma,
    early detection is crucial for effective treatment and improving patient survival rates. In fact, Malignant
    melanoma accounts for only 2.3% of all skin cancers yet is responsible for more than 75% of skin cancer-related deaths.
    However, if it is detected at an early stage, it is highly curable; the 10-year survival rate is between 90% and 97% when
    the tumour thickness is…

    Skin cancer is a growing public health concern; while some types of skin cancer are deadly, such as Melanoma,
    early detection is crucial for effective treatment and improving patient survival rates. In fact, Malignant
    melanoma accounts for only 2.3% of all skin cancers yet is responsible for more than 75% of skin cancer-related deaths.
    However, if it is detected at an early stage, it is highly curable; the 10-year survival rate is between 90% and 97% when
    the tumour thickness is less than 1 mm. Also, the treatment for an early detected cancerous mole is as simple as excision
    of the lesion, which can prevent metastasis and spread of cancer to other organs. In this research study, we introduce an
    approach for skin cancer classification using a state-of-the-art deep learning architecture that has demonstrated
    exceptional performance in diverse image analysis tasks. We have used two publicly available benchmark data sets for
    training and validating our results: HAM10000 and ISIC2018 datasets. These datasets consist of dermoscopic images
    captured using Dermatoscopes and carefully annotated by expert dermatologists. Preprocessing techniques, such as
    normalization and augmentation, are applied to enhance the robustness and generalization of the model. The proposed
    approach demonstrated the efficacy of extracting relevant features for accurate classification by leveraging Deep Object
    Detection models to identify the location of the Lesion, then using the Segment Anything Model (SAM) and MedSAM
    for extracting the border of the lesions, then finally using various pre-trained states-of-the-art Deep Convolution
    Networks for Classification. Comprehensive experiments and evaluations are performed in this research; the results
    demonstrate the effectiveness of using Zero-Shot Segmentation methods over traditional deep learning architectures in
    skin cancer classification.

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  • Classification of MRI Brain Tumor images Using Deep Learning Segment Anything Model for Segmentation and Deep Convolution Neural Network

    World Journal of Advanced Research and Reviews (WJARR)

    Brain tumors pose a significant health challenge by putting pressure on healthy parts of the brain or spreading into
    other areas and blocking the flow of fluid around the brain. Thus, identifying and categorizing the tumor is crucial for
    delivering effective treatment, especially if detected early. This means the tumor is smaller, and treatment is more
    effective, less invasive, and has fewer side effects.
    In recent years, many researchers have developed computer vision, and more…

    Brain tumors pose a significant health challenge by putting pressure on healthy parts of the brain or spreading into
    other areas and blocking the flow of fluid around the brain. Thus, identifying and categorizing the tumor is crucial for
    delivering effective treatment, especially if detected early. This means the tumor is smaller, and treatment is more
    effective, less invasive, and has fewer side effects.
    In recent years, many researchers have developed computer vision, and more specifically, deep learning methods, to
    automate the analysis of brain MRI scans. These methods enable efficient processing and improve the accuracy of
    detecting small tumors.
    This paper aims to propose a deep-learning method for classifying brain tumors. In this work, the input image goes
    through two subprocesses: first, object detection to identify the tumor's location. Then, a fine-tuned Segment Anything
    Model (SAM) was applied to extract the lesion from the background. Finally, deep learning Convolution Neural Network
    (CNN), is applied to the cropped image for classification. This method will help doctors and researchers detect tumors
    at the initial stages

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  • A Cloud-Based Infrastructure for Feedback-Driven Training and Image Recognition

    Volume 216: MEDINFO 2015: eHealth-enabled Health

    Abstract
    Advanced techniques in machine learning combined with scalable “cloud” computing infrastructure are driving the creation of new and innovative health diagnostic applications.
    We describe a service and application for performing image training and recognition, tailored to dermatology and melanoma identification. The system implements new machine learning approaches to provide a feedback-driven training loop. This training sequence enhances classification performance
    by…

    Abstract
    Advanced techniques in machine learning combined with scalable “cloud” computing infrastructure are driving the creation of new and innovative health diagnostic applications.
    We describe a service and application for performing image training and recognition, tailored to dermatology and melanoma identification. The system implements new machine learning approaches to provide a feedback-driven training loop. This training sequence enhances classification performance
    by incrementally retraining the classifier model from expert responses. To easily provide this application and associated web service to clinical practices, we also describe a scalable cloud infrastructure, deployable in public cloud infrastructure and private, on-premise systems

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  • Ibm research australia at lifeclef2014: Plant identification task

    Working notes of CLEF 2014

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  • "GPU-accelerated eXtended Classifier System"

    Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2013)

    مؤلفون آخرون
    • Michael Kirley
    • Raymond Chiong
    • T. Weise
  • “High performance computing enabling exhaustive analysis of higher order single nucleotide polymorphism interaction in Genome Wide Association Studies”

    HISA 2013

    مؤلفون آخرون
  • “IBM Research at ImageCLEF 2013 Medical Tasks”

    American Medical Informatics Association

    مؤلفون آخرون
  • “Incorporating feature ranking and evolutionary methods for the classification of high-dimensional DNA microarray gene expression data”

    Australas Medical Journal

    مؤلفون آخرون
    • Michael Kirley
    • Raymond Chiong
  • “An enhanced XCS rule discovery module using feature ranking”

    International Journal of Machine Learning and Cybernetics

    مؤلفون آخرون
    • Michael Kirley
  • “FS-XCS vs. GRD-XCS: An analysis using high-dimensional DNA microarray gene expression data sets”

    Second Australian Workshop on Artificial Intelligence in Health (AIH 2012)

    مؤلفون آخرون
    • Michael Kirley
    • Raymond Chiong
  • “Guided rule discovery in XCS for High-dimensional Classification Problems”

    Proceedings of 24th Australasian Artificial Intelligence Conference

    مؤلفون آخرون
    • Michael Kirely
  • "A multiple population XCS: Evolving condition-action rules based on feature space partitions"

    IEEE World Congress on Computational Intelligence - CEC 2010

    مؤلفون آخرون
    • Michael Kirley
  • “Gene Expression Classification with a novel coevolutionary based learning classifier system on Public Clouds”

    IEEE e-science 2010 workshop of “Parallel Optimization and Parameter Fitting"

    مؤلفون آخرون
  • “CoXCS: a coevolutionary learning classifier based on feature space partitioning”

    Australasian Conference on Artificial Intelligence 2009

    مؤلفون آخرون
    • Michael Kirely
  • “Using data mining and Genetic fuzzy systems technique in order to predict cash flow in financial institutes and Banks”

    10’th annual conference of Computer Society of Iran

    مؤلفون آخرون
  • “Using Cresceptron neural network for Farsi character recognition”

    Negasht- Journal of Computer Society of Iran

    مؤلفون آخرون
    • Eslame Nazemi

براءات الاختراع

  • Detection of outlier lesions based on extracted features from skin images

    تاريخ الإصدار ⁦ US 10586330

    A method for image analysis comprises receiving one or more images of a plurality of lesions captured from a body of a person, extracting one or more features of the plurality of lesions from the one or more images, analyzing the extracted one or more features, wherein the analyzing comprises determining a distance between at least two lesions with respect to the extracted one or more features, and determining whether any of the plurality of lesions is an outlier based on the analyzing.

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  • Imaging segmentation using multi-scale machine learning approach

    تاريخ الإصدار ⁦ US 10402979

    A robust segmentation technique based on multi-layer classification technique to identify the lesion boundary is described. The inventors have discovered a technique based on training several classifiers such that to classify each pixel as lesion versus normal Each classifier is trained on a specific range of image resolutions. Then, for a new test image, the trained classifiers are applied on the image. Then by fusing the prediction results in pixel level a probability map is generated. In the…

    A robust segmentation technique based on multi-layer classification technique to identify the lesion boundary is described. The inventors have discovered a technique based on training several classifiers such that to classify each pixel as lesion versus normal Each classifier is trained on a specific range of image resolutions. Then, for a new test image, the trained classifiers are applied on the image. Then by fusing the prediction results in pixel level a probability map is generated. In the next step, a thresholding method is applied to convert the probability map to a binary mask, which determines a mole border.

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  • SYSTEM, METHOD, AND RECORDING MEDIUM FOR DYNAMICALLY CHANGING SEARCH RESULT DELIVERY FORMAT

    تاريخ الإصدار ⁦ US 10387538

    A result format modifying method, system, and non-transitory computer readable medium, include an extracting circuit configured to extract a plurality of format types of a search result conducted by a user, a determining circuit configured to determine user activity based on user data, and a deciding circuit configured to decide a format of the plurality of format types to deliver to the user based on a time interval between a current time and a start time of the user's next activity as…

    A result format modifying method, system, and non-transitory computer readable medium, include an extracting circuit configured to extract a plurality of format types of a search result conducted by a user, a determining circuit configured to determine user activity based on user data, and a deciding circuit configured to decide a format of the plurality of format types to deliver to the user based on a time interval between a current time and a start time of the user's next activity as determined by the determining circuit.

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  • Skin scanning device with hair orientation and view angle changes

    تاريخ الإصدار ⁦ US 10368751

    A scan head for scanning skin includes a frame and a camera coupled to the frame. A controllable probe is coupled to the frame and is configured to change an orientation of hair on the skin to be examined and imaged with the camera.

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  • Automated skin lesion segmentation using deep side layers

    تاريخ الإصدار ⁦ US 10373312

    A method for computer-aided diagnosis of skin lesions includes obtaining a dermoscopic image, convolving the dermoscopic image in a plurality of convolutional layers, obtaining deconvolved outputs of at least two convolutional layers of the plurality of convolutional layers, obtaining side-output feature maps by applying loss functions to the deconvolved outputs of the at least two convolutional layers, obtaining a first concatenated feature map by concatenating the side-output feature maps…

    A method for computer-aided diagnosis of skin lesions includes obtaining a dermoscopic image, convolving the dermoscopic image in a plurality of convolutional layers, obtaining deconvolved outputs of at least two convolutional layers of the plurality of convolutional layers, obtaining side-output feature maps by applying loss functions to the deconvolved outputs of the at least two convolutional layers, obtaining a first concatenated feature map by concatenating the side-output feature maps with different first weights, obtaining a second concatenated feature map by concatenating the side-output feature maps with different second weights, and producing a final score map by convolving the first and second concatenated feature maps in a final convolutional layer followed by a loss layer. Also disclosed: a computer-readable medium embodying instructions for the method, and an apparatus configured to implement the method.

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  • COGNITIVE SCREENING FOR PROHIBITED ITEMS ACROSS MULTIPLE CHECKPOINTS BY USING CONTEXT AWARE SPATIO-TEMPORAL ANALYSIS

    تاريخ الإصدار ⁦ US 20190156449

    A computer-implemented screening method, system, and computer program product including detecting at least two disassembled components of an object spread across multiple sensor scan images of at least one container, storing a record of each disassembled component correlated with contextual information of each owner of each container corresponding to a sensor scan image, and flagging the owner of the container in the sensor scan image based on a joint assessment of the sensor scan image…

    A computer-implemented screening method, system, and computer program product including detecting at least two disassembled components of an object spread across multiple sensor scan images of at least one container, storing a record of each disassembled component correlated with contextual information of each owner of each container corresponding to a sensor scan image, and flagging the owner of the container in the sensor scan image based on a joint assessment of the sensor scan image including a disassembled component that can be combined with another disassembled component in a different sensor scan image to assemble the object and the contextual information of each owner.

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  • Risk assessment based on patient similarity determined using image analysis

    تاريخ الإصدار ⁦ US 10283221

    A method for risk assessment comprises receiving one or more images of a plurality of lesions captured from a body of a target person, generating one or more digital signatures based on the one or more images from the body of the target person, comparing the generated one or more digital signatures to digital signatures of respective reference persons, wherein the comparing comprises measuring similarities between the generated one or more digital signatures and the digital signatures of the…

    A method for risk assessment comprises receiving one or more images of a plurality of lesions captured from a body of a target person, generating one or more digital signatures based on the one or more images from the body of the target person, comparing the generated one or more digital signatures to digital signatures of respective reference persons, wherein the comparing comprises measuring similarities between the generated one or more digital signatures and the digital signatures of the respective reference persons, and determining a risk factor for the target person of developing a disease based on the measured similarities and predetermined risk factors of developing the disease for the reference persons.

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  • System and method for lesion analysis and recommendation of screening checkpoints for reduced risk of skin cancer

    تاريخ الإصدار ⁦ US 10282843

    A method for image analysis comprises receiving one or more current images of a lesion from a body of a person, wherein the one or more current images are electronically captured by and transmitted from a capture device, and analyzing the one or more current images, wherein the analyzing comprises performing image processing to compare the one or more current images captured at a first time to one or more previous images of the lesion captured at a second time prior to the first time, and…

    A method for image analysis comprises receiving one or more current images of a lesion from a body of a person, wherein the one or more current images are electronically captured by and transmitted from a capture device, and analyzing the one or more current images, wherein the analyzing comprises performing image processing to compare the one or more current images captured at a first time to one or more previous images of the lesion captured at a second time prior to the first time, and determining at least one difference between the one or more current images and the one or more previous images based on the comparing. The method further comprises determining a probability that the lesion will become diseased based on the analysis, and recommending a time for a future image capture of the lesion and/or a consultation with a practitioner based on the determined probability.

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  • Detection of outlier lesions based on extracted features from skin images

    تاريخ الإصدار ⁦ US 10242442

    A method for image analysis comprises receiving one or more images of a plurality of lesions captured from a body of a person, extracting one or more features of the plurality of lesions from the one or more images, analyzing the extracted one or more features, wherein the analyzing comprises determining a distance between at least two lesions with respect to the extracted one or more features, and determining whether any of the plurality of lesions is an outlier based on the analyzing.

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  • STRUCTURE-PRESERVING COMPOSITE MODEL FOR SKIN LESION SEGMENTATION

    تاريخ الإصدار ⁦ US 20170243345

    A structure-preserving composite model for skin lesion segmentation includes partitioning a dermoscopic image into superpixels at a first scale. Each superpixel is a vertex on a graph defined by color coordinates and spatial coordinates, and represents a number of pixels of the dermoscopic image according to the first scale. Further, constructing a plurality of k background templates by k-means clustering selected ones of the superpixels in space and color. Additionally, generating sparse…

    A structure-preserving composite model for skin lesion segmentation includes partitioning a dermoscopic image into superpixels at a first scale. Each superpixel is a vertex on a graph defined by color coordinates and spatial coordinates, and represents a number of pixels of the dermoscopic image according to the first scale. Further, constructing a plurality of k background templates by k-means clustering selected ones of the superpixels in space and color. Additionally, generating sparse representations of the plurality of superpixels based on the plurality of background templates. Also, calculating a reconstruction error for each superpixel by comparison of its sparse representation to its original color coordinates and spatial coordinates. Furthermore, outputting a confidence map that identifies each pixel of the dermoscopic image as belonging or not belonging to a skin lesion, based on the reconstruction errors of the representative superpixels.

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  • MOBILE DEVICE INPUT LANGUAGE SUGGESTION BASED ON MESSAGE RECEIVER'S ENVIRONMENT

    تاريخ الإصدار ⁦ US 20180302362

    At a sending electronic device, from a remote location, an indication is received of an environment at a receiving mobile electronic device to which the sending electronic device is to send a message. It is determined how to send the message from the sending electronic device to the receiving mobile electronic device, based on the indication of the environment at the receiving mobile electronic device. The message is sent from the sending electronic device to the receiving mobile electronic…

    At a sending electronic device, from a remote location, an indication is received of an environment at a receiving mobile electronic device to which the sending electronic device is to send a message. It is determined how to send the message from the sending electronic device to the receiving mobile electronic device, based on the indication of the environment at the receiving mobile electronic device. The message is sent from the sending electronic device to the receiving mobile electronic device in accordance with the determining step.

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  • Annotation of skin image using learned feature representation

    تاريخ الإصدار ⁦ US 9886758

    A method for annotation of skin images includes receiving a plurality of dermatoscopic images. Each of the dermatoscopic includes a region of lesion skin and a region of normal skin. A first convolutional neural network is trained according to an interior of the region of lesion skin using each of the plurality of dermatoscopic images. A second convolutional neural network is trained according to a boundary between the region of lesion skin and the region of normal skin. An additional…

    A method for annotation of skin images includes receiving a plurality of dermatoscopic images. Each of the dermatoscopic includes a region of lesion skin and a region of normal skin. A first convolutional neural network is trained according to an interior of the region of lesion skin using each of the plurality of dermatoscopic images. A second convolutional neural network is trained according to a boundary between the region of lesion skin and the region of normal skin. An additional dermatoscopic image is acquired. The first and second convolutional neural networks are used to identify a region of lesion skin within the acquired additional dermatoscopic image.

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  • Machine training and search engine for providing specialized cognitive healthcare apparatus

    تاريخ الإصدار ⁦ US 9785749

    Training a machine to provide specialized health care apparatus may include receiving text describing a user's health condition via a user interface. Text may be converted into corresponding medical terms. A database may be searched for a list of health care providers treating health conditions associated with the medical terms. A machine learning model may be built that may include user preference for a predefined set of features associated with the user's health condition and health care…

    Training a machine to provide specialized health care apparatus may include receiving text describing a user's health condition via a user interface. Text may be converted into corresponding medical terms. A database may be searched for a list of health care providers treating health conditions associated with the medical terms. A machine learning model may be built that may include user preference for a predefined set of features associated with the user's health condition and health care provider preference for the predefined set of features in treating the user's health condition. The machine learning model may predict one or more of the health care providers that provide treatment for the user's health condition that matches the user's preference. The machine learning model may be retrained based on one or more of feedback from the user, the health care providers, and updated traits of the users and the health care providers.

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  • Cloud-based infrastructure for feedback-driven training and image recognition

    تاريخ الإصدار ⁦ US 9760990

    A method for a cloud-based feedback-driven image training and recognition includes receiving a set of expert annotations of a plurality of training images of a predetermined subject matter, wherein the expert annotations include a clinical diagnosis for each image or region of interest in an image, training one or more classification models from the set of expert annotations, testing the one or more classification models on a plurality of test images that are different from the training images,…

    A method for a cloud-based feedback-driven image training and recognition includes receiving a set of expert annotations of a plurality of training images of a predetermined subject matter, wherein the expert annotations include a clinical diagnosis for each image or region of interest in an image, training one or more classification models from the set of expert annotations, testing the one or more classification models on a plurality of test images that are different from the training images, wherein each classification model yields a clinical diagnosis for each image and a confidence score for that diagnosis, and receiving expert classification result feedback regarding the clinical diagnosis for each image and a confidence score yielded by each classification model.

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  • SOLAR IRRADIATION MODELING AND FORECASTING USING COMMUNITY BASED TERRESTRIAL SKY IMAGING

    قدم ⁦ US 20170351970

    Solar irradiation may be predicted based on input terrestrial sky images comprising cloud images, the terrestrial sky images taken from a plurality of geographic locations by a plurality of devices; for example, wherein the terrestrial sky images are crowd sourced from the plurality of devices. A model may be generated that predicts solar irradiation in a geographic area based on the input terrestrial sky images and the geographic locations from where the terrestrial sky images were taken. A…

    Solar irradiation may be predicted based on input terrestrial sky images comprising cloud images, the terrestrial sky images taken from a plurality of geographic locations by a plurality of devices; for example, wherein the terrestrial sky images are crowd sourced from the plurality of devices. A model may be generated that predicts solar irradiation in a geographic area based on the input terrestrial sky images and the geographic locations from where the terrestrial sky images were taken. A signal representing the solar irradiation predicted by the model is output.

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التكريمات والمكافآت

  • First Patent award

    GE

  • Manager Bravo award

    GE Aviation

    Developing Deep Learning roadmap for GE Aviation digital

  • Invited Speaker to "Emerging Big Data Technologies Summit 2016 (EBDTS'16)"​

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  • IBM invention achievement award, Second Plateau

    IBM

  • First Patent file issued

    IBM

  • IBM invention achievement award, First Plateau

    IBM

  • First patent award

    IBM

  • Manager’s Choice Award – 2015 Unite to Get it Done Now

    IBM

  • Melbourne University MIFRS and MIRS Scholarships (2008-2012)

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  • Victorian Life Science Computation Initiative (VLSCI) travel scholarship to SC10, New Orleans

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التوصيات المستلمة

عرض ملف Mani الشخصي الكامل

  • مشاهدة الأشخاص المشتركين الذين تعرفهم
  • تقديم تعارف
  • تواصل مع Mani مباشرة
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