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aliasghar khani home about publication projects contact welcome i m aliasghar khani machine learning researcher aliasghar khani machine learning researcher currently i am pursuing a master s degree at simon fraser university where i have the privilege of being supervised by professor ghassan hamarneh with dr saeid asgari as my co supervisor my academic journey began at sharif university of technology sut where i completed my bachelor s degree in computer engineering from 2016 to 2021 working under the guidance of professor mohammadhossein rohban for my b s project during my b s project i focused on leveraging self supervised and weakly supervised methods to enhance adversarially trained models this research aimed to address the challenges associated with adversarial training by exploring alternative learning approaches computer vision is my primary area of interest and research i am particularly intrigued by the fundamental problems within this field such as robustness and bias additionally i have practical experience in combining language models with computer vision techniques which has allowed me to explore the intersection of these domains one of my greatest passions is continuous learning and i am committed to utilizing my knowledge to tackle significant real world problems both through collaborative teamwork and individual efforts in summary my research interests encompass robustness self supervised learning generalization my skills coding skills python 95 c c 80 javascript 60 matlab 70 other skills mathematics knowledge 80 machine learning knowledge 90 machine learning frameworks 90 education 2021 current computer science simon fraser university m sc student under supervision of professor ghassan hamarneh 2016 2021 computer engineering sharif university of technology b sc in computer engineering 2012 2016 mathematics and physics shahid madani high school experience 2022 ai research internship autodesk under the supervision of dr saeid asgari 2020 2021 research at bioinformatics and computational biology bcb lab sharif university of technology under the supervision of prof mohammad hossein rohban 2019 2021 ai researcher iran s national elites foundation under the supervision of prof mahdieh soleymani baghshah publication slime segment like me iclr2024 semantic segmentation with an arbitrary granularity is a challenging task we introduce slime segment like me which can segment an image according to a given sample by leveraging diffusion models cross self attention and prompt optimization link to paper masktune mitigating spurious correlations by forcing to explore neurips2022 this work proposes masktune a masking strategy that prevents over reliance on spurious or a limited number of features masktune forces the trained model to explore new features during a single epoch finetuning by masking previously discovered features masktune unlike earlier approaches for mitigating shortcut learning does not require any supervision link to paper counterbalancing teacher regularizing batch normalized models for robustness arxiv2022 in this work we investigate low generalization performance for bn by first showing that removing bn layers across a wide range of architectures leads to lower out of domain and corruption errors at the cost of higher in domain errors we then propose counterbalancing teacher ct for increasing robustness link to paper paip 2020 microsatellite instability prediction in colorectal cancer medical image analysis in this work we introduce the paip 2020 challenge which addresses colon cancer cases affected by msi micro satellite instability to our knowledge this is the inaugural challenge that integrates image segmentation and genetic data this paper encompasses details about the organizing team s activities the colon cancer cohort measurement methodologies participant algorithms and a comprehensive evaluation of each algorithm s performance additionally this paper presents the algorithms utilized by the top 10 ranked participants in the challenge link to paper monusac2020 a multi organ nuclei segmentation and classification challenge ieee transactions on medical imaging 2021 automating the tasks of detecting segmenting and classifying nuclei can free up the pathologists time for higher value tasks and reduce errors due to fatigue and subjectivity we prepared a large and diverse dataset of nucleus boundary annotations and class labels and organized a challenge around this dataset in this paper we summarize the dataset and the key findings of the challenge link to paper a deep learning framework for viable tumor burden estimation icspis2020 in this paper we propose a deep learning framework for the segmentation of whole and viable tumor areas of liver cancer from whole slide images wsis to this end we use a fast segmentation convolutional neural network fast scnn as our network after data augmentation on the training subset we train the network with a multi term loss function and swa technique link to paper towards automatic prostate gleason grading via deep convolutional neural networks icspis2019 in terms of accuracy microscopic inspection of biopsy tissues is best the gleason grading system is used to evaluate the stage of prostate cancer using prostate biopsy samples it is time consuming to grade each region in a tissue in this paper we propose an automatic method for this task based on a deep learning based approach the mobilenetv2 backbone is used with the deeplabv3 model link to paper projects see my work all website biomedical classification information retrieval system indexes wikipedia s data with text classification clustering system and a crawler that crawls pages and indexes in elasticsearch hospital patients reception simulation simulator for the reception of patients in a hospital iot monitoring device iot device fro measuring soil temperature and humidity in the greenhouse and sends them to a web server corona virus segmentation and classification researched segmentation and classification of coronavirus in ct scan images cancerous area detection website website for analyzing histopathology tissue images and detects cancerous portions bag of classifiers we implemented perceptron mira and kernelized classifiers in python on mnist and fashion datasets contact me where to find me vancouver b c canada follow me linkedin github google scholar contact me khani dot ali1376 at gmail dot com copyright 2022 macode id