Search Results for author: Md Mahfuzur Rahman Siddiquee

Found 19 papers, 11 papers with code

AnoFPDM: Anomaly Segmentation with Forward Process of Diffusion Models for Brain MRI

1 code implementation24 Apr 2024 Yiming Che, Fazle Rafsani, Jay Shah, Md Mahfuzur Rahman Siddiquee, Teresa Wu

To tackle this challenge, we introduce Anomaly Segmentation with Forward Process of Diffusion Models (AnoFPDM), a fully weakly-supervised framework that operates without the need of pixel-level labels.

Segmentation

Ordinal Classification with Distance Regularization for Robust Brain Age Prediction

1 code implementation IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023 Jay Shah, Md Mahfuzur Rahman Siddiquee, Yi Su, Teresa Wu, Baoxin Li

However, these methods are subject to an inherent regression to the mean effect, which causes a systematic bias resulting in an overestimation of brain age in young subjects and underestimation in old subjects.

Age Estimation Ordinal Classification

Domain-knowledge Inspired Pseudo Supervision (DIPS) for Unsupervised Image-to-Image Translation Models to Support Cross-Domain Classification

2 code implementations18 Mar 2023 Firas Al-Hindawi, Md Mahfuzur Rahman Siddiquee, Teresa Wu, Han Hu, Ying Sun

Cross-domain classification frameworks were developed to handle this data domain shift problem by utilizing unsupervised image-to-image translation models to translate an input image from the unlabeled domain to the labeled domain.

domain classification Translation +1

Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR Images

1 code implementation18 Feb 2023 Md Mahfuzur Rahman Siddiquee, Jay Shah, Teresa Wu, Catherine Chong, Todd J. Schwedt, Gina Dumkrieger, Simona Nikolova, Baoxin Li

Harnessing the power of deep neural networks in the medical imaging domain is challenging due to the difficulties in acquiring large annotated datasets, especially for rare diseases, which involve high costs, time, and effort for annotation.

Alzheimer's Disease Detection Anomaly Detection +3

Automated head and neck tumor segmentation from 3D PET/CT

1 code implementation22 Sep 2022 Andriy Myronenko, Md Mahfuzur Rahman Siddiquee, Dong Yang, Yufan He, Daguang Xu

Head and neck tumor segmentation challenge (HECKTOR) 2022 offers a platform for researchers to compare their solutions to segmentation of tumors and lymph nodes from 3D CT and PET images.

Segmentation Tumor Segmentation

Automated segmentation of intracranial hemorrhages from 3D CT

no code implementations21 Sep 2022 Md Mahfuzur Rahman Siddiquee, Dong Yang, Yufan He, Daguang Xu, Andriy Myronenko

Intracranial hemorrhage segmentation challenge (INSTANCE 2022) offers a platform for researchers to compare their solutions to segmentation of hemorrhage stroke regions from 3D CTs.

Segmentation

HealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human Disease

1 code implementation5 Sep 2022 Md Mahfuzur Rahman Siddiquee, Jay Shah, Teresa Wu, Catherine Chong, Todd Schwedt, Baoxin Li

Therefore, this paper poses the research question of how to improve unsupervised anomaly detection by utilizing (1) an unannotated set of mixed images, in addition to (2) the set of healthy images as being used in the literature.

Image-to-Image Translation Translation +1

Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs

no code implementations1 Nov 2021 Md Mahfuzur Rahman Siddiquee, Andriy Myronenko

Another year of the multimodal brain tumor segmentation challenge (BraTS) 2021 provides an even larger dataset to facilitate collaboration and research of brain tumor segmentation methods, which are necessary for disease analysis and treatment planning.

Brain Tumor Segmentation Decoder +2

A2B-GAN: Utilizing Unannotated Anomalous Images for Anomaly Detection in Medical Image Analysis

no code implementations29 Sep 2021 Md Mahfuzur Rahman Siddiquee, Teresa Wu, Baoxin Li

This paper poses the research question of how to improve anomaly detection by using an unannotated set of mixed images of both normal and anomalous samples (in addition to a set of normal images from healthy subjects).

Anomaly Detection Image-to-Image Translation +1

Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis

2 code implementations19 Aug 2019 Zongwei Zhou, Vatsal Sodha, Md Mahfuzur Rahman Siddiquee, Ruibin Feng, Nima Tajbakhsh, Michael B. Gotway, Jianming Liang

More importantly, learning a model from scratch simply in 3D may not necessarily yield performance better than transfer learning from ImageNet in 2D, but our Models Genesis consistently top any 2D approaches including fine-tuning the models pre-trained from ImageNet as well as fine-tuning the 2D versions of our Models Genesis, confirming the importance of 3D anatomical information and significance of our Models Genesis for 3D medical imaging.

Anatomy Brain Tumor Segmentation +6

Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization

1 code implementation ICCV 2019 Md Mahfuzur Rahman Siddiquee, Zongwei Zhou, Nima Tajbakhsh, Ruibin Feng, Michael B. Gotway, Yoshua Bengio, Jianming Liang

Qualitative and quantitative evaluations demonstrate that the proposed method outperforms the state of the art in multi-domain image-to-image translation and that it surpasses predominant weakly-supervised localization methods in both disease detection and localization.

domain classification Image-to-Image Translation +1

UNet++: A Nested U-Net Architecture for Medical Image Segmentation

33 code implementations18 Jul 2018 Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, Jianming Liang

Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet

Image Segmentation Segmentation +3

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