Search Results for author: Lei Bi

Found 33 papers, 17 papers with code

Enhancing medical vision-language contrastive learning via inter-matching relation modelling

no code implementations19 Jan 2024 Mingjian Li, Mingyuan Meng, Michael Fulham, David Dagan Feng, Lei Bi, Jinman Kim

These learned image representations can be transferred to and benefit various downstream medical vision tasks such as disease classification and segmentation.

Contrastive Learning Cross-Modal Retrieval +3

Full-resolution MLPs Empower Medical Dense Prediction

1 code implementation28 Nov 2023 Mingyuan Meng, Yuxin Xue, Dagan Feng, Lei Bi, Jinman Kim

This textural information is crucial for medical dense prediction as it can differentiate the subtle human anatomy in medical images.

Anatomy Image Restoration

PET Synthesis via Self-supervised Adaptive Residual Estimation Generative Adversarial Network

no code implementations24 Oct 2023 Yuxin Xue, Lei Bi, Yige Peng, Michael Fulham, David Dagan Feng, Jinman Kim

We introduce (1) An adaptive residual estimation mapping mechanism, AE-Net, designed to dynamically rectify the preliminary synthesized PET images by taking the residual map between the low-dose PET and synthesized output as the input, and (2) A self-supervised pre-training strategy to enhance the feature representation of the coarse generator.

Generative Adversarial Network

AutoFuse: Automatic Fusion Networks for Deformable Medical Image Registration

1 code implementation11 Sep 2023 Mingyuan Meng, Michael Fulham, Dagan Feng, Lei Bi, Jinman Kim

However, DNN-based registration needs to explore the spatial information of each image and fuse this information to characterize spatial correspondence.

Deformable Medical Image Registration Image Registration +1

Non-iterative Coarse-to-fine Transformer Networks for Joint Affine and Deformable Image Registration

1 code implementation7 Jul 2023 Mingyuan Meng, Lei Bi, Michael Fulham, Dagan Feng, Jinman Kim

Recently, Non-Iterative Coarse-to-finE (NICE) registration methods have been proposed to perform coarse-to-fine registration in a single network and showed advantages in both registration accuracy and runtime.

Image Registration

AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT Images

2 code implementations17 May 2023 Mingyuan Meng, Bingxin Gu, Michael Fulham, Shaoli Song, Dagan Feng, Lei Bi, Jinman Kim

Instead of adopting MTL, we propose a novel Segmentation-to-Survival Learning (SSL) strategy, where our AdaMSS is trained for tumor segmentation and survival prediction sequentially in two stages.

Multi-Task Learning Segmentation +2

Brain Tumor Sequence Registration with Non-iterative Coarse-to-fine Networks and Dual Deep Supervision

1 code implementation15 Nov 2022 Mingyuan Meng, Lei Bi, Dagan Feng, Jinman Kim

In this study, we focus on brain tumor sequence registration between pre-operative and follow-up Magnetic Resonance Imaging (MRI) scans of brain glioma patients, in the context of Brain Tumor Sequence Registration challenge (BraTS-Reg 2022).

Radiomics-enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck Cancer

2 code implementations10 Nov 2022 Mingyuan Meng, Lei Bi, Dagan Feng, Jinman Kim

Recently, deep learning methods have been proposed to perform end-to-end outcome prediction so as to remove the reliance on manual segmentation.

Multi-Task Learning Segmentation +1

Hyper-Connected Transformer Network for Multi-Modality PET-CT Segmentation

no code implementations28 Oct 2022 Lei Bi, Michael Fulham, Shaoli Song, David Dagan Feng, Jinman Kim

We also introduced a hyper connected fusion to fuse the contextual and complementary image features across multiple transformers in an iterative manner.

Segmentation Tumor Segmentation

Non-iterative Coarse-to-fine Registration based on Single-pass Deep Cumulative Learning

1 code implementation25 Jun 2022 Mingyuan Meng, Lei Bi, Dagan Feng, Jinman Kim

Recently, iterative deep registration methods have been used to alleviate this limitation, where the transformations are iteratively learned in a coarse-to-fine manner.

Image Registration

Unsupervised Landmark Detection Based Spatiotemporal Motion Estimation for 4D Dynamic Medical Images

2 code implementations30 Sep 2021 Yuyu Guo, Lei Bi, Dongming Wei, Liyun Chen, Zhengbin Zhu, Dagan Feng, Ruiyan Zhang, Qian Wang, Jinman Kim

In the first stage, we process the raw dense image to extract sparse landmarks to represent the target organ anatomical topology and discard the redundant information that is unnecessary for motion estimation.

Anatomy Motion Estimation +1

DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT

2 code implementations16 Sep 2021 Mingyuan Meng, Bingxin Gu, Lei Bi, Shaoli Song, David Dagan Feng, Jinman Kim

However, the models using the whole target regions will also include non-relevant background information, while the models using segmented tumor regions will disregard potentially prognostic information existing out of primary tumors (e. g., local lymph node metastasis and adjacent tissue invasion).

Computed Tomography (CT) Multi-Task Learning +3

Predicting Distant Metastases in Soft-Tissue Sarcomas from PET-CT scans using Constrained Hierarchical Multi-Modality Feature Learning

no code implementations23 Apr 2021 Yige Peng, Lei Bi, Ashnil Kumar, Michael Fulham, Dagan Feng, Jinman Kim

Most CNNs are designed for single-modality imaging data (CT or PET alone) and do not exploit the information embedded in PET-CT where there is a combination of an anatomical and functional imaging modality.

Management STS

Enhancing Medical Image Registration via Appearance Adjustment Networks

2 code implementations9 Mar 2021 Mingyuan Meng, Lei Bi, Michael Fulham, David Dagan Feng, Jinman Kim

In this study, we propose an Appearance Adjustment Network (AAN) to enhance the adaptability of DLRs to appearance variations.

Anatomy Computational Efficiency +2

Deep Multi-Scale Resemblance Network for the Sub-class Differentiation of Adrenal Masses on Computed Tomography Images

no code implementations29 Jul 2020 Lei Bi, Jinman Kim, Tingwei Su, Michael Fulham, David Dagan Feng, Guang Ning

The application of CNNs, to adrenal masses is challenging due to large intra-class variations, large inter-class similarities and imbalanced training data due to the size of the mass lesions.

Computed Tomography (CT) Management

A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image

1 code implementation CVPR 2020 Yuyu Guo, Lei Bi, Euijoon Ahn, Dagan Feng, Qian Wang, Jinman Kim

SVIN introduces dual networks: first is the spatiotemporal motion network that leverages the 3D convolutional neural network (CNN) for unsupervised parametric volumetric registration to derive spatiotemporal motion field from two-image volumes; the second is the sequential volumetric interpolation network, which uses the derived motion field to interpolate image volumes, together with a new regression-based module to characterize the periodic motion cycles in functional organ structures.


High-parallelism Inception-like Spiking Neural Networks for Unsupervised Feature Learning

1 code implementation2 Dec 2019 Mingyuan Meng, Xingyu Yang, Lei Bi, Jinman Kim, Shanlin Xiao, Zhiyi Yu

Most STDP-based SNNs adopted a slow-learning Fully-Connected (FC) architecture and used a sub-optimal vote-based scheme for spike decoding.

General Classification Image Classification +1

Automated Segmentation of the Optic Disk and Cup using Dual-Stage Fully Convolutional Networks

no code implementations13 Feb 2019 Lei Bi, Yuyu Guo, Qian Wang, Dagan Feng, Michael Fulham, Jinman Kim

Our approach leverages deep residual architectures and FCNs and learns and infers the location of the optic cup and disk in a step-wise manner with fine-grained details.

Segmentation Test

The Liver Tumor Segmentation Benchmark (LiTS)

6 code implementations13 Jan 2019 Patrick Bilic, Patrick Christ, Hongwei Bran Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, Fabian Lohöfer, Julian Walter Holch, Wieland Sommer, Felix Hofmann, Alexandre Hostettler, Naama Lev-Cohain, Michal Drozdzal, Michal Marianne Amitai, Refael Vivantik, Jacob Sosna, Ivan Ezhov, Anjany Sekuboyina, Fernando Navarro, Florian Kofler, Johannes C. Paetzold, Suprosanna Shit, Xiaobin Hu, Jana Lipková, Markus Rempfler, Marie Piraud, Jan Kirschke, Benedikt Wiestler, Zhiheng Zhang, Christian Hülsemeyer, Marcel Beetz, Florian Ettlinger, Michela Antonelli, Woong Bae, Míriam Bellver, Lei Bi, Hao Chen, Grzegorz Chlebus, Erik B. Dam, Qi Dou, Chi-Wing Fu, Bogdan Georgescu, Xavier Giró-i-Nieto, Felix Gruen, Xu Han, Pheng-Ann Heng, Jürgen Hesser, Jan Hendrik Moltz, Christian Igel, Fabian Isensee, Paul Jäger, Fucang Jia, Krishna Chaitanya Kaluva, Mahendra Khened, Ildoo Kim, Jae-Hun Kim, Sungwoong Kim, Simon Kohl, Tomasz Konopczynski, Avinash Kori, Ganapathy Krishnamurthi, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Li, John Lowengrub, Jun Ma, Klaus Maier-Hein, Kevis-Kokitsi Maninis, Hans Meine, Dorit Merhof, Akshay Pai, Mathias Perslev, Jens Petersen, Jordi Pont-Tuset, Jin Qi, Xiaojuan Qi, Oliver Rippel, Karsten Roth, Ignacio Sarasua, Andrea Schenk, Zengming Shen, Jordi Torres, Christian Wachinger, Chunliang Wang, Leon Weninger, Jianrong Wu, Daguang Xu, Xiaoping Yang, Simon Chun-Ho Yu, Yading Yuan, Miao Yu, Liping Zhang, Jorge Cardoso, Spyridon Bakas, Rickmer Braren, Volker Heinemann, Christopher Pal, An Tang, Samuel Kadoury, Luc Soler, Bram van Ginneken, Hayit Greenspan, Leo Joskowicz, Bjoern Menze

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.

Benchmarking Computed Tomography (CT) +3

Improving Automatic Skin Lesion Segmentation using Adversarial Learning based Data Augmentation

no code implementations23 Jul 2018 Lei Bi, Dagan Feng, Jinman Kim

Segmentation of skin lesions is considered as an important step in computer aided diagnosis (CAD) for automated melanoma diagnosis.

Data Augmentation Generative Adversarial Network +4

Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)

2 code implementations31 Jul 2017 Lei Bi, Jinman Kim, Ashnil Kumar, Dagan Feng, Michael Fulham

Positron emission tomography (PET) image synthesis plays an important role, which can be used to boost the training data for computer aided diagnosis systems.

Computed Tomography (CT) Image Generation

Automatic Liver Lesion Detection using Cascaded Deep Residual Networks

no code implementations10 Apr 2017 Lei Bi, Jinman Kim, Ashnil Kumar, Dagan Feng

Recently, deep learning methods based on fully convolutional networks (FCNs) have been successful in many segmentation problems primarily because they leverage a large labelled dataset to hierarchically learn the features that best correspond to the shallow visual appearance as well as the deep semantics of the areas to be segmented.

Lesion Detection Segmentation +1

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