Search Results for author: Mohammad Yaqub

Found 35 papers, 15 papers with code

Fine-Tuned Large Language Models for Symptom Recognition from Spanish Clinical Text

no code implementations28 Jan 2024 Mai A. Shaaban, Abbas Akkasi, Adnan Khan, Majid Komeili, Mohammad Yaqub

The accurate recognition of symptoms in clinical reports is significantly important in the fields of healthcare and biomedical natural language processing.

Retrieval

Multi-Task Learning Approach for Unified Biometric Estimation from Fetal Ultrasound Anomaly Scans

no code implementations16 Nov 2023 Mohammad Areeb Qazi, Mohammed Talha Alam, Ibrahim Almakky, Werner Gerhard Diehl, Leanne Bricker, Mohammad Yaqub

Precise estimation of fetal biometry parameters from ultrasound images is vital for evaluating fetal growth, monitoring health, and identifying potential complications reliably.

Classification Multi-Task Learning +1

FUSC: Fetal Ultrasound Semantic Clustering of Second Trimester Scans Using Deep Self-supervised Learning

1 code implementation19 Oct 2023 Hussain Alasmawi, Leanne Bricker, Mohammad Yaqub

This study presents an unsupervised approach for automatically clustering ultrasound images into a large range of fetal views, reducing or eliminating the need for manual labeling.

Clustering Self-Supervised Learning

UniLVSeg: Unified Left Ventricular Segmentation with Sparsely Annotated Echocardiogram Videos through Self-Supervised Temporal Masking and Weakly Supervised Training

no code implementations30 Sep 2023 Fadillah Maani, Asim Ukaye, Nada Saadi, Numan Saeed, Mohammad Yaqub

From calculating biomarkers such as ejection fraction to the probability of a patient's heart failure, accurate segmentation of the heart and its structures allows doctors to plan and execute treatments with greater precision and accuracy.

Left Ventricle Segmentation LV Segmentation +2

PECon: Contrastive Pretraining to Enhance Feature Alignment between CT and EHR Data for Improved Pulmonary Embolism Diagnosis

1 code implementation27 Aug 2023 Santosh Sanjeev, Salwa K. Al Khatib, Mai A. Shaaban, Ibrahim Almakky, Vijay Ram Papineni, Mohammad Yaqub

Previous deep learning efforts have focused on improving the performance of Pulmonary Embolism(PE) diagnosis from Computed Tomography (CT) scans using Convolutional Neural Networks (CNN).

Computed Tomography (CT) Contrastive Learning +1

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

no code implementations11 Aug 2023 Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah, Alejandro F Frangi, Alena Buyx, Anais Emelie, Andrea Lara, Antonio R Porras, An-Wen Chan, Arcadi Navarro, Ben Glocker, Benard O Botwe, Bishesh Khanal, Brigit Beger, Carol C Wu, Celia Cintas, Curtis P Langlotz, Daniel Rueckert, Deogratias Mzurikwao, Dimitrios I Fotiadis, Doszhan Zhussupov, Enzo Ferrante, Erik Meijering, Eva Weicken, Fabio A González, Folkert W Asselbergs, Fred Prior, Gabriel P Krestin, Gary Collins, Geletaw S Tegenaw, Georgios Kaissis, Gianluca Misuraca, Gianna Tsakou, Girish Dwivedi, Haridimos Kondylakis, Harsha Jayakody, Henry C Woodruf, Hugo JWL Aerts, Ian Walsh, Ioanna Chouvarda, Irène Buvat, Islem Rekik, James Duncan, Jayashree Kalpathy-Cramer, Jihad Zahir, Jinah Park, John Mongan, Judy W Gichoya, Julia A Schnabel, Kaisar Kushibar, Katrine Riklund, Kensaku MORI, Kostas Marias, Lameck M Amugongo, Lauren A Fromont, Lena Maier-Hein, Leonor Cerdá Alberich, Leticia Rittner, Lighton Phiri, Linda Marrakchi-Kacem, Lluís Donoso-Bach, Luis Martí-Bonmatí, M Jorge Cardoso, Maciej Bobowicz, Mahsa Shabani, Manolis Tsiknakis, Maria A Zuluaga, Maria Bielikova, Marie-Christine Fritzsche, Marius George Linguraru, Markus Wenzel, Marleen de Bruijne, Martin G Tolsgaard, Marzyeh Ghassemi, Md Ashrafuzzaman, Melanie Goisauf, Mohammad Yaqub, Mohammed Ammar, Mónica Cano Abadía, Mukhtar M E Mahmoud, Mustafa Elattar, Nicola Rieke, Nikolaos Papanikolaou, Noussair Lazrak, Oliver Díaz, Olivier Salvado, Oriol Pujol, Ousmane Sall, Pamela Guevara, Peter Gordebeke, Philippe Lambin, Pieta Brown, Purang Abolmaesumi, Qi Dou, Qinghua Lu, Richard Osuala, Rose Nakasi, S Kevin Zhou, Sandy Napel, Sara Colantonio, Shadi Albarqouni, Smriti Joshi, Stacy Carter, Stefan Klein, Steffen E Petersen, Susanna Aussó, Suyash Awate, Tammy Riklin Raviv, Tessa Cook, Tinashe E M Mutsvangwa, Wendy A Rogers, Wiro J Niessen, Xènia Puig-Bosch, Yi Zeng, Yunusa G Mohammed, Yves Saint James Aquino, Zohaib Salahuddin, Martijn P A Starmans

This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.

Fairness

LegoNet: Alternating Model Blocks for Medical Image Segmentation

no code implementations6 Jun 2023 Ikboljon Sobirov, Cheng Xie, Muhammad Siddique, Parijat Patel, Kenneth Chan, Thomas Halborg, Christos Kotanidis, Zarqiash Fatima, Henry West, Keith Channon, Stefan Neubauer, Charalambos Antoniades, Mohammad Yaqub

Since the emergence of convolutional neural networks (CNNs), and later vision transformers (ViTs), the common paradigm for model development has always been using a set of identical block types with varying parameters/hyper-parameters.

Image Segmentation Medical Image Segmentation +2

Balancing Privacy and Performance for Private Federated Learning Algorithms

no code implementations11 Apr 2023 Xiangjian Hou, Sarit Khirirat, Mohammad Yaqub, Samuel Horvath

Our findings reveal a direct correlation between the optimal number of local steps, communication rounds, and a set of variables, e. g the DP privacy budget and other problem parameters, specifically in the context of strongly convex optimization.

Federated Learning

Weakly Unsupervised Domain Adaptation for Vestibular Schwannoma Segmentation

no code implementations13 Mar 2023 Shahad Hardan, Hussain Alasmawi, Xiangjian Hou, Mohammad Yaqub

In this work, we propose a weakly supervised machine learning approach that learns from only ceT1 scans and adapts to segment two structures from hrT2 scans: the VS and the cochlea from the crossMoDA dataset.

Unsupervised Domain Adaptation

CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

no code implementations11 Mar 2023 Simon Graham, Quoc Dang Vu, Mostafa Jahanifar, Martin Weigert, Uwe Schmidt, Wenhua Zhang, Jun Zhang, Sen yang, Jinxi Xiang, Xiyue Wang, Josef Lorenz Rumberger, Elias Baumann, Peter Hirsch, Lihao Liu, Chenyang Hong, Angelica I. Aviles-Rivero, Ayushi Jain, Heeyoung Ahn, Yiyu Hong, Hussam Azzuni, Min Xu, Mohammad Yaqub, Marie-Claire Blache, Benoît Piégu, Bertrand Vernay, Tim Scherr, Moritz Böhland, Katharina Löffler, Jiachen Li, Weiqin Ying, Chixin Wang, Dagmar Kainmueller, Carola-Bibiane Schönlieb, Shuolin Liu, Dhairya Talsania, Yughender Meda, Prakash Mishra, Muhammad Ridzuan, Oliver Neumann, Marcel P. Schilling, Markus Reischl, Ralf Mikut, Banban Huang, Hsiang-Chin Chien, Ching-Ping Wang, Chia-Yen Lee, Hong-Kun Lin, Zaiyi Liu, Xipeng Pan, Chu Han, Jijun Cheng, Muhammad Dawood, Srijay Deshpande, Raja Muhammad Saad Bashir, Adam Shephard, Pedro Costa, João D. Nunes, Aurélio Campilho, Jaime S. Cardoso, Hrishikesh P S, Densen Puthussery, Devika R G, Jiji C V, Ye Zhang, Zijie Fang, Zhifan Lin, Yongbing Zhang, Chunhui Lin, Liukun Zhang, Lijian Mao, Min Wu, Vi Thi-Tuong Vo, Soo-Hyung Kim, Taebum Lee, Satoshi Kondo, Satoshi Kasai, Pranay Dumbhare, Vedant Phuse, Yash Dubey, Ankush Jamthikar, Trinh Thi Le Vuong, Jin Tae Kwak, Dorsa Ziaei, Hyun Jung, Tianyi Miao, David Snead, Shan E Ahmed Raza, Fayyaz Minhas, Nasir M. Rajpoot

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome.

Nuclear Segmentation Segmentation +2

FUSQA: Fetal Ultrasound Segmentation Quality Assessment

no code implementations8 Mar 2023 Sevim Cengiz, Ibrahim Almakky, Mohammad Yaqub

In this paper, we propose a simplified Fetal Ultrasound Segmentation Quality Assessment (FUSQA) model to tackle the segmentation quality assessment when no masks exist to compare with.

Age Estimation Segmentation

Learning Confident Classifiers in the Presence of Label Noise

no code implementations2 Jan 2023 Asma Ahmed Hashmi, Aigerim Zhumabayeva, Nikita Kotelevskii, Artem Agafonov, Mohammad Yaqub, Maxim Panov, Martin Takáč

We evaluate the proposed method on a series of classification tasks such as noisy versions of MNIST, CIFAR-10, Fashion-MNIST datasets as well as CIFAR-10N, which is real-world dataset with noisy human annotations.

Image Segmentation Medical Image Segmentation +2

On the Importance of Image Encoding in Automated Chest X-Ray Report Generation

1 code implementation24 Nov 2022 Otabek Nazarov, Mohammad Yaqub, Karthik Nandakumar

Chest X-ray is one of the most popular medical imaging modalities due to its accessibility and effectiveness.

Text Generation

How to Train Vision Transformer on Small-scale Datasets?

2 code implementations13 Oct 2022 Hanan Gani, Muzammal Naseer, Mohammad Yaqub

However, in contrast to convolutional neural networks, Vision Transformer lacks inherent inductive biases.

Self-omics: A Self-supervised Learning Framework for Multi-omics Cancer Data

1 code implementation3 Oct 2022 Sayed Hashim, Karthik Nandakumar, Mohammad Yaqub

Lack of annotated data is a significant problem in machine learning, and Self-Supervised Learning (SSL) methods are typically used to deal with limited labelled data.

Cancer type classification Self-Supervised Learning +1

TMSS: An End-to-End Transformer-based Multimodal Network for Segmentation and Survival Prediction

1 code implementation12 Sep 2022 Numan Saeed, Ikboljon Sobirov, Roba Al Majzoub, Mohammad Yaqub

We propose TMSS, an end-to-end Transformer based Multimodal network for Segmentation and Survival prediction that leverages the superiority of transformers that lies in their abilities to handle different modalities.

Multimodal Deep Learning Segmentation +2

EchoCoTr: Estimation of the Left Ventricular Ejection Fraction from Spatiotemporal Echocardiography

1 code implementation9 Sep 2022 Rand Muhtaseb, Mohammad Yaqub

On the other hand, vision transformers can incorporate global details and long sequences but are computationally expensive and typically require more data to train.

Video Understanding

Self-Ensembling Vision Transformer (SEViT) for Robust Medical Image Classification

1 code implementation4 Aug 2022 Faris Almalik, Mohammad Yaqub, Karthik Nandakumar

Vision Transformers (ViT) are competing to replace Convolutional Neural Networks (CNN) for various computer vision tasks in medical imaging such as classification and segmentation.

Image Classification Medical Image Classification

GARDNet: Robust Multi-View Network for Glaucoma Classification in Color Fundus Images

1 code implementation25 May 2022 Ahmed Al Mahrooqi, Dmitrii Medvedev, Rand Muhtaseb, Mohammad Yaqub

Glaucoma is one of the most severe eye diseases, characterized by rapid progression and leading to irreversible blindness.

Color Space-based HoVer-Net for Nuclei Instance Segmentation and Classification

no code implementations3 Mar 2022 Hussam Azzuni, Muhammad Ridzuan, Min Xu, Mohammad Yaqub

Nuclei segmentation and classification is the first and most crucial step that is utilized for many different microscopy medical analysis applications.

Instance Segmentation Segmentation +1

An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data

1 code implementation25 Feb 2022 Numan Saeed, Roba Al Majzoub, Ikboljon Sobirov, Mohammad Yaqub

The main issue when using clinical and imaging data to train a deep learning model is to decide on how to combine the information from these sources.

SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification

1 code implementation3 Feb 2022 Sayed Hashim, Muhammad Ali, Karthik Nandakumar, Mohammad Yaqub

In our project, we extend the idea of using a VAE model for low dimensional latent space extraction with the self-supervised learning technique of feature subsetting.

Cancer type classification Decision Making +2

Hyperparameter Optimization for COVID-19 Chest X-Ray Classification

no code implementations26 Jan 2022 Ibraheem Hamdi, Muhammad Ridzuan, Mohammad Yaqub

Despite the introduction of vaccines, Coronavirus disease (COVID-19) remains a worldwide dilemma, continuously developing new variants such as Delta and the recent Omicron.

Binary Classification Classification +1

Deep Learning-based Quality Assessment of Clinical Protocol Adherence in Fetal Ultrasound Dating Scans

no code implementations17 Jan 2022 Sevim Cengiz, Mohammad Yaqub

To assess fetal health during pregnancy, doctors use the gestational age (GA) calculation based on the Crown Rump Length (CRL) measurement in order to check for fetal size and growth trajectory.

Automatic Segmentation of Head and Neck Tumor: How Powerful Transformers Are?

no code implementations17 Jan 2022 Ikboljon Sobirov, Otabek Nazarov, Hussain Alasmawi, Mohammad Yaqub

Cancer is one of the leading causes of death worldwide, and head and neck (H&N) cancer is amongst the most prevalent types.

Segmentation Tumor Segmentation

Is it Possible to Predict MGMT Promoter Methylation from Brain Tumor MRI Scans using Deep Learning Models?

no code implementations16 Jan 2022 Numan Saeed, Shahad Hardan, Kudaibergen Abutalip, Mohammad Yaqub

A couple of recent publications proposed a connection between the MGMT promoter state and the MRI scans of the tumor and hence suggested the use of deep learning models for this purpose.

Contrastive Pretraining for Echocardiography Segmentation with Limited Data

1 code implementation16 Jan 2022 Mohamed Saeed, Rand Muhtaseb, Mohammad Yaqub

Our results show that contrastive pretraining helps improve the performance on left ventricle segmentation, particularly when annotated data is scarce.

Contrastive Learning Image Segmentation +4

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