Search Results for author: Jinman Kim

Found 50 papers, 20 papers with code

Dual-modal Dynamic Traceback Learning for Medical Report Generation

no code implementations24 Jan 2024 Shuchang Ye, Mingyuan Meng, Mingjian Li, Dagan Feng, Jinman Kim

Recent generative representation learning methods have demonstrated the benefits of dual-modal learning from both image and text modalities.

Medical Report Generation Representation Learning

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

A Dual-branch Self-supervised Representation Learning Framework for Tumour Segmentation in Whole Slide Images

no code implementations20 Mar 2023 Hao Wang, Euijoon Ahn, Jinman Kim

These SSL approaches, however, are not designed for handling multi-resolution WSIs, which limits their performance in learning discriminative image features.

Representation Learning Self-Supervised Learning +1

A Review of Predictive and Contrastive Self-supervised Learning for Medical Images

no code implementations10 Feb 2023 Wei-Chien Wang, Euijoon Ahn, Dagan Feng, Jinman Kim

Over the last decade, supervised deep learning on manually annotated big data has been progressing significantly on computer vision tasks.

Self-Supervised Learning

UNAEN: Unsupervised Abnormality Extraction Network for MRI Motion Artifact Reduction

no code implementations4 Jan 2023 Yusheng Zhou, Hao Li, Jianan Liu, Zhengmin Kong, Tao Huang, Euijoon Ahn, Zhihan Lv, Jinman Kim, David Dagan Feng

Our results substantiate the potential of UNAEN as a promising solution applicable in real-world clinical environments, with the capability to enhance diagnostic accuracy and facilitate image-guided therapies.

Z-SSMNet: A Zonal-aware Self-Supervised Mesh Network for Prostate Cancer Detection and Diagnosis in bpMRI

no code implementations12 Dec 2022 Yuan Yuan, Euijoon Ahn, Dagan Feng, Mohamad Khadra, Jinman Kim

However, existing state of the art AI algorithms which are based on deep learning technology are often limited to 2D images that fails to capture inter-slice correlations in 3D volumetric images.

Self-Supervised Learning

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 Representation Learning for 3D MRI Super Resolution with Degradation Adaptation

no code implementations13 May 2022 Jianan Liu, Hao Li, Tao Huang, Euijoon Ahn, Kang Han, Adeel Razi, Wei Xiang, Jinman Kim, David Dagan Feng

However, the difference in degradation representations between synthetic and authentic LR images suppresses the quality of SR images reconstructed from authentic LR images.

Image Registration Representation Learning +1

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

Self-supervised Representation Learning Framework for Remote Physiological Measurement Using Spatiotemporal Augmentation Loss

1 code implementation16 Jul 2021 Hao Wang, Euijoon Ahn, Jinman Kim

To address these problems, we present a novel self-supervised spatiotemporal learning framework for remote physiological signal representation learning, where there is a lack of labelled training data.

Contrastive Learning Data Augmentation +3

A Spatial Guided Self-supervised Clustering Network for Medical Image Segmentation

1 code implementation11 Jul 2021 Euijoon Ahn, Dagan Feng, Jinman Kim

Hence, we propose a new spatial guided self-supervised clustering network (SGSCN) for medical image segmentation, where we introduce multiple loss functions designed to aid in grouping image pixels that are spatially connected and have similar feature representations.

Clustering Image Segmentation +3

Benchmarking for Biomedical Natural Language Processing Tasks with a Domain Specific ALBERT

1 code implementation9 Jul 2021 Usman Naseem, Adam G. Dunn, Matloob Khushi, Jinman Kim

We present BioALBERT, a domain-specific adaptation of A Lite Bidirectional Encoder Representations from Transformers (ALBERT), trained on biomedical (PubMed and PubMed Central) and clinical (MIMIC-III) corpora and fine tuned for 6 different tasks across 20 benchmark datasets.

Benchmarking Document Classification +7

Classifying vaccine sentiment tweets by modelling domain-specific representation and commonsense knowledge into context-aware attentive GRU

no code implementations17 Jun 2021 Usman Naseem, Matloob Khushi, Jinman Kim, Adam G. Dunn

In this study, to classify vaccine sentiment tweets with limited information, we present a novel end-to-end framework consisting of interconnected components that use domain-specific LM trained on vaccine-related tweets and models commonsense knowledge into a bidirectional gated recurrent network (CK-BiGRU) with context-aware attention.

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

BioALBERT: A Simple and Effective Pre-trained Language Model for Biomedical Named Entity Recognition

no code implementations19 Sep 2020 Usman Naseem, Matloob Khushi, Vinay Reddy, Sakthivel Rajendran, Imran Razzak, Jinman Kim

In recent years, with the growing amount of biomedical documents, coupled with advancement in natural language processing algorithms, the research on biomedical named entity recognition (BioNER) has increased exponentially.

Language Modelling named-entity-recognition +3

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.

Anatomy

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

Unsupervised Feature Learning with K-means and An Ensemble of Deep Convolutional Neural Networks for Medical Image Classification

no code implementations7 Jun 2019 Euijoon Ahn, Ashnil Kumar, Dagan Feng, Michael Fulham, Jinman Kim

Hence, we propose a new unsupervised feature learning method that learns feature representations to then differentiate dissimilar medical images using an ensemble of different convolutional neural networks (CNNs) and K-means clustering.

Clustering General Classification +2

Unsupervised Deep Transfer Feature Learning for Medical Image Classification

no code implementations15 Mar 2019 Euijoon Ahn, Ashnil Kumar, Dagan Feng, Michael Fulham, Jinman Kim

The accuracy and robustness of image classification with supervised deep learning are dependent on the availability of large-scale, annotated training data.

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

Co-Learning Feature Fusion Maps from PET-CT Images of Lung Cancer

1 code implementation5 Oct 2018 Ashnil Kumar, Michael Fulham, Dagan Feng, Jinman Kim

Our aim is to improve fusion of the complementary information in multi-modality PET-CT with a new supervised convolutional neural network (CNN) that learns to fuse complementary information for multi-modality medical image analysis.

Tumor Segmentation

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

Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis

no code implementations16 Jul 2018 Euijoon Ahn, Jinman Kim, Ashnil Kumar, Michael Fulham, Dagan Feng

The availability of large-scale annotated image datasets and recent advances in supervised deep learning methods enable the end-to-end derivation of representative image features that can impact a variety of image analysis problems.

Medical Image Retrieval Retrieval

An unsupervised long short-term memory neural network for event detection in cell videos

no code implementations7 Sep 2017 Ha Tran Hong Phan, Ashnil Kumar, David Feng, Michael Fulham, Jinman Kim

We compared our method to several published supervised methods evaluated on the same dataset and to a supervised LSTM method with a similar design and configuration to our unsupervised method.

Event Detection

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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