Search Results for author: Nima Tajbakhsh

Found 14 papers, 5 papers with code

MDT-Net: Multi-domain Transfer by Perceptual Supervision for Unpaired Images in OCT Scan

no code implementations12 Mar 2022 Weinan Song, Gaurav Fotedar, Nima Tajbakhsh, Ziheng Zhou, Xiaowei Ding

We also take the transformed results as additional training images for fluid segmentation in OCT scans in the tasks of domain adaptation and data augmentation.

Data Augmentation Domain Adaptation

A Location-Sensitive Local Prototype Network for Few-Shot Medical Image Segmentation

1 code implementation18 Mar 2021 Qinji Yu, Kang Dang, Nima Tajbakhsh, Demetri Terzopoulos, Xiaowei Ding

Despite the tremendous success of deep neural networks in medical image segmentation, they typically require a large amount of costly, expert-level annotated data.

Image Segmentation Medical Image Segmentation +1

Extreme Consistency: Overcoming Annotation Scarcity and Domain Shifts

no code implementations15 Apr 2020 Gaurav Fotedar, Nima Tajbakhsh, Shilpa Ananth, Xiaowei Ding

In this paper, we introduce \emph{extreme consistency}, which overcomes the above limitations, by maximally leveraging unlabeled data from the same or a different domain in a teacher-student semi-supervised paradigm.

Retinal Vessel Segmentation

ErrorNet: Learning error representations from limited data to improve vascular segmentation

no code implementations10 Oct 2019 Nima Tajbakhsh, Brian Lai, Shilpa Ananth, Xiaowei Ding

In this paper, we propose a segmentation framework called ErrorNet, which learns to correct these segmentation mistakes through the repeated process of injecting systematic segmentation errors to the segmentation result based on a learned shape prior, followed by attempting to predict the injected error.

Domain Adaptation Retinal Vessel Segmentation

Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation

no code implementations27 Aug 2019 Nima Tajbakhsh, Laura Jeyaseelan, Qian Li, Jeffrey Chiang, Zhihao Wu, Xiaowei Ding

The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks.

Image Segmentation Medical Image Segmentation +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.

Image-to-Image Translation Translation

Automatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network

no code implementations18 Feb 2019 Abdullah-Al-Zubaer Imran, Ali Hatamizadeh, Shilpa P. Ananth, Xiaowei Ding, Demetri Terzopoulos, Nima Tajbakhsh

We evaluated our model using 84 chest CT scans from the LIDC and 154 pathological cases from the LTRC datasets.

Surrogate Supervision for Medical Image Analysis: Effective Deep Learning From Limited Quantities of Labeled Data

no code implementations25 Jan 2019 Nima Tajbakhsh, Yufei Hu, Junli Cao, Xingjian Yan, Yi Xiao, Yong Lu, Jianming Liang, Demetri Terzopoulos, Xiaowei Ding

We investigate the effectiveness of a simple solution to the common problem of deep learning in medical image analysis with limited quantities of labeled training data.

Colorization Transfer Learning

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

25 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 Semantic Segmentation +2

Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

no code implementations2 Jun 2017 Nima Tajbakhsh, Jae Y. Shin, Suryakanth R. Gurudu, R. Todd Hurst, Christopher B. Kendall, Michael B. Gotway, Jianming Liang

Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence.

Transfer Learning

Automating Carotid Intima-Media Thickness Video Interpretation with Convolutional Neural Networks

no code implementations CVPR 2016 Jae Y. Shin, Nima Tajbakhsh, R. Todd Hurst, Christopher B. Kendall, Jianming Liang

However, each CIMT examination includes several ultrasound videos, and interpreting each of these CIMT videos involves three operations: (1) select three end-diastolic ultrasound frames (EUF) in the video, (2) localize a region of interest (ROI) in each selected frame, and (3) trace the lumen-intima interface and the media-adventitia interface in each ROI to measure CIMT.

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