“I had the pleasure working with Mani for 3+ years at GE Aviation digital, collaborating on several projects and proof of concepts. His deep expertise in Deep Learning and Computer Vision made him a goto person in the group. He developed expertise in predictive maintenance in Aircraft /Engines Maintenance domain and showcased many PoC’s using deep learning for anomaly detection. Mani has special quality of attention to detail along with quick implementation of concepts which makes him unique. He has also shown his eagerness to learn Microsoft Azure and completed the DP100 certification in no-time. I am sure he will be a great asset to any organization from data science perspective. ”
نبذة عني
Steering data science and AI initives, my leadership pivots on innovating with Generative…
الخبرة
التعليم
التراخيص والشهادات
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From Notebook to Kubeflow Pipelines with MiniKF and Kale
Arrikto Academy
المنشورات
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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. -
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 -
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 systemsمؤلفون آخرونعرض المنشور -
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 -
“Incorporating feature ranking and evolutionary methods for the classification of high-dimensional DNA microarray gene expression data”
Australas Medical Journal
مؤلفون آخرون -
“An enhanced XCS rule discovery module using feature ranking”
International Journal of Machine Learning and Cybernetics
مؤلفون آخرون -
“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 -
“Guided rule discovery in XCS for High-dimensional Classification Problems”
Proceedings of 24th Australasian Artificial Intelligence Conference
مؤلفون آخرون -
"A multiple population XCS: Evolving condition-action rules based on feature space partitions"
IEEE World Congress on Computational Intelligence - CEC 2010
مؤلفون آخرون -
“CoXCS: a coevolutionary learning classifier based on feature space partitioning”
Australasian Conference on Artificial Intelligence 2009
مؤلفون آخرون -
“Using Cresceptron neural network for Farsi character recognition”
Negasht- Journal of Computer Society of Iran
مؤلفون آخرون
براءات الاختراع
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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.
التكريمات والمكافآت
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First Patent award
GE
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Manager Bravo award
GE Aviation
Developing Deep Learning roadmap for GE Aviation digital
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Invited Speaker to "Emerging Big Data Technologies Summit 2016 (EBDTS'16)"
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IBM invention achievement award, Second Plateau
IBM
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First Patent file issued
IBM
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IBM invention achievement award, First Plateau
IBM
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First patent award
IBM
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Manager’s Choice Award – 2015 Unite to Get it Done Now
IBM
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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
17شخصا قدموا توصية لـMani
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