Sumin Jung

Doanh Cao Bui

Anh Tien Nguyen

Jae Ung Lee

Ju Cheon Lee

Yejin Jeon

Ju Heon Oh

Chang Hee Han

Hoang Long Le

Nhu Nhat Tan Doan

Minh Nguyen Nhat To

Quoc Dang Vu

Eal Kim

The primary focus of our research is to develop the cutting-edge artificial intelligence and deep learning techniques for a wide range of data and applications.

인공지능 및 딥러닝 기술을 개발하여 광범위한 데이터와 응용 분야에 적용하는 것이 우리 연구의 주요 목표입니다.

Two-Stage Residual-Guided Diffusion Framework for High-Fidelity Pathology Image Restoration

Lightweight Look-ahead Multi-context Vision Transformer

Spatially-constrained and-unconstrained bi-graph interaction network for multi-organ pathology image classification

Performance Analysis and Comparison of Knowledge Distillation Among Diverse Teacher and Student Models

Dual-Encoding Style Transfer for Korean Font Generation

다중 기관에서의 디지털 병리 암 분화도 예측을 위한 멀티 태스크 기반 단일 모델 학습

디지털 병리 대장암 분화도 예측을 위한 순서학습 기반 비전 트랜스포머 기술

Development of a software application for the segmentation of a cerebral infarction from 3D diffusion weighted imaging

비전 트랜스포머 기반 디지털 병리 대장암 진단 모델

선명도에 따른 조직 이미지 분류: 딥러닝 모델과 이미지 품질 측정 기법의 성능 비교

의료 영상 병변 영상 분할을 위한 효율적 딥러닝 모델 연구

the Case of the Waste Agricultural Film in Korea

이종 영상 데이터를 활용한 흉부 X-ray 코로나 바이러스 질병 탐지 모델

디지털 병리 대장암 진단을 위한 삼중항 손실을 이용한 심층 메트릭 학습

디지털 병리 이미지 분석을 위한 Mamba 기반 접근법 비교

Region-aggregated attention CNN for disease detection in fruit images

3-D multitask deep neural networks for collateral imaging from dynamic susceptibility contrast-enhanced magnetic resonance perfusion

Normal and abnormal pathology knowledge-augmented vision-language model for anomaly detection in pathology images

뇌졸중 환자의 뇌 병변 분할 영상 예측을 위한 딥러닝 모델 및 학습 방법

Distance ordinal regression loss for an improved nuclei segmentation

SONNET: A self-guided ordinal regression neural network for segmentation and classification of nuclei in large-scale multi-tissue histology images

Mecformer: Multi-task whole slide image classification with expert consultation network

Semi-supervised learning for an improved diagnosis of COVID-19 in CT images

Order-ViT: Order learning vision transformer for cancer classification in pathology images

QuIIL at T3 Challenge: Towards

Multi-scale binary pattern encoding network for cancer classification in pathology images

From Pathology to Radiology: Evaluating the Applicability of Pathology Foundation

NucleiMix: Realistic Data Augmentation for Nuclei Instance Segmentation

Boosting Knowledge Distillation

ViDRiP-LLaVA: A Dataset and Benchmark for Diagnostic Reasoning from Pathology Videos

Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images

Bach: Grand challenge on breast cancer histology images

A multi-organ nucleus segmentation challenge

Methods for segmentation and classification of digital microscopy tissue images

PAIP 2019: Liver cancer segmentation challenge

Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles

Leukocytes classification and segmentation in microscopic blood smear: a resource-aware healthcare service in smart cities

Deep recurrent neural networks for prostate cancer detection: analysis of temporal enhanced ultrasound

Multimodal microscopy for automated histologic analysis of prostate cancer

Automated prostate cancer detection using T 2‐weighted and high‐ b ‐value diffusion‐weighted magnetic resonance imaging

Convolutional neural network based deep-learning architecture for prostate cancer detection on multiparametric magnetic resonance images

Improving prediction of prostate cancer recurrence using chemical imaging

Nuclear architecture analysis of prostate cancer via convolutional neural networks

A novel dataset and efficient deep learning framework for automated grading of renal cell carcinoma from kidney histopathology images

Multiview boosting digital pathology analysis of prostate cancer

CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting

Detection of prostate cancer in multiparametric MRI using random forest with instance weighting

Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging

Detection and grading of prostate cancer using temporal enhanced ultrasound: combining deep neural networks and tissue mimicking simulations

Jin Tae Kwak

Nuclear segmentation and classification within Haematoxylin & Eosin stained histology images is a fundamental prerequisite in the digital pathology work-flow. The development of automated methods for nuclear segmentation and classification enables the quantitative analysis of tens of thousands of nuclei within a whole-slide pathology image, opening up possibilities of further analysis of large-scale nuclear morphometry. However, automated nuclear segmentation and classification is faced with a major challenge in that there are several different types of nuclei, some of them exhibiting large intra-class variability such as the nuclei of tumour cells. Additionally, some of the nuclei are often clustered together. To address these challenges, we present a novel convolutional neural network for simultaneous nuclear segmentation and classification that leverages the instance-rich information encoded within the vertical …

Breast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysis by pathologists, in a task that i) is highly time- and cost-consuming and ii) often leads to nonconsensual results. The relevance and potential of automatic classification algorithms using hematoxylin-eosin stained histopathological images has already been demonstrated, but the reported results are still sub-optimal for clinical use. With the goal of advancing the state-of-the-art in automatic classification, the Grand Challenge on BreAst Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018). BACH aimed at the classification and localization of clinically relevant …

Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere …

High-resolution microscopy images of tissue specimens provide detailed information about the morphology of normal and diseased tissue. Image analysis of tissue morphology can help cancer researchers develop a better understanding of cancer biology. Segmentation of nuclei and classification of tissue images are two common tasks in tissue image analysis. Development of accurate and efficient algorithms for these tasks is a challenging problem because of the complexity of tissue morphology and tumor heterogeneity. In this paper we present two computer algorithms; one designed for segmentation of nuclei and the other for classification of whole slide tissue images. The segmentation algorithm implements a multiscale deep residual aggregation network to accurately segment nuclear material and then separate clumped nuclei into individual nuclei. The classification algorithm initially carries out patch-level classification via a deep learning method, then patch-level statistical and morphological features are used as input to a random forest regression model for whole slide image classification. The segmentation and classification algorithms were evaluated in the MICCAI 2017 Digital Pathology challenge. The segmentation algorithm achieved an accuracy score of 0.78. The classification algorithm achieved an accuracy score of 0.81. These scores were the highest in the challenge.

Pathology Artificial Intelligence Platform (PAIP) is a free research platform in support of pathological artificial intelligence (AI). The main goal of the platform is to construct a high-quality pathology learning data set that will allow greater accessibility. The PAIP Liver Cancer Segmentation Challenge, organized in conjunction with the Medical Image Computing and Computer Assisted Intervention Society (MICCAI 2019), is the first image analysis challenge to apply PAIP datasets. The goal of the challenge was to evaluate new and existing algorithms for automated detection of liver cancer in whole-slide images (WSIs). Additionally, the PAIP of this year attempted to address potential future problems of AI applicability in clinical settings. In the challenge, participants were asked to use analytical data and statistical metrics to evaluate the performance of automated algorithms in two different tasks. The participants were …

Recently, unmanned aerial vehicles (UAVs) have gained much attention. In particular, there is a growing interest in utilizing UAVs for agricultural applications such as crop monitoring and management. We propose a computerized system that is capable of detecting Fusarium wilt of radish with high accuracy. The system adopts computer vision and machine learning techniques, including deep learning, to process the images captured by UAVs at low altitudes and to identify the infected radish. The whole radish field is first segmented into three distinctive regions (radish, bare ground, and mulching film) via a softmax classifier and -means clustering. Then, the identified radish regions are further classified into healthy radish and Fusarium wilt of radish using a deep convolutional neural network (CNN). In identifying radish, bare ground, and mulching film from a radish field, we achieved an accuracy of . In …

Smart cities are a future reality for municipalities around the world. Healthcare services play a vital role in the transformation of traditional cities into smart cities. In this paper, we present a ubiquitous and quality computer-aided blood analysis service for the detection and counting of white blood cells (WBCs) in blood samples. WBCs also called leukocytes or leucocytes are the cells of the immune system that are involved in protecting the body against both infectious disease and foreign invaders. Analysis of leukocytes provides valuable information to medical specialists, helping them in diagnosing different important hematic diseases, such as AIDS and blood cancer (Leukaemia). However, this task is prone to errors and can be time-consuming. A mobile-cloud-assisted detection and classification of leukocytes from blood smear images can enhance accuracy and speed up the detection of WBCs. In this paper, we …

Temporal enhanced ultrasound (TeUS), comprising the analysis of variations in backscattered signals from a tissue over a sequence of ultrasound frames, has been previously proposed as a new paradigm for tissue characterization. In this paper, we propose to use deep recurrent neural networks (RNN) to explicitly model the temporal information in TeUS. By investigating several RNN models, we demonstrate that long short-term memory (LSTM) networks achieve the highest accuracy in separating cancer from benign tissue in the prostate. We also present algorithms for in-depth analysis of LSTM networks. Our in vivo study includes data from 255 prostate biopsy cores of 157 patients. We achieve area under the curve, sensitivity, specificity, and accuracy of 0.96, 0.76, 0.98, and 0.93, respectively. Our result suggests that temporal modeling of TeUS using RNN can significantly improve cancer detection accuracy …

Background Prostate cancer is the single most prevalent cancer in US men whose gold standard of diagnosis is histologic assessment of biopsies. Manual assessment of stained tissue of all biopsies limits speed and accuracy in clinical practice and research of prostate cancer diagnosis. We sought to develop a fully-automated multimodal microscopy method to distinguish cancerous from non-cancerous tissue samples. Methods We recorded chemical data from an unstained tissue microarray (TMA) using Fourier transform infrared (FT-IR) spectroscopic imaging. Using pattern recognition, we identified epithelial cells without user input. We fused the cell type information with the corresponding stained images commonly used in clinical practice. Extracted morphological features, optimized by two-stage feature selection method using a minimum-redundancy-maximal-relevance (mRMR) criterion and sequential floating …

Purpose The authors propose a computer‐aided diagnosis (CAD) system for prostate cancer to aid in improving the accuracy, reproducibility, and standardization of multiparametric magnetic resonance imaging (MRI). Methods The proposed system utilizes two MRI sequences [ T 2‐weighted MRI and high‐ b ‐value ( b = 2000 s/mm 2 ) diffusion‐weighted imaging (DWI)] and texture features based on local binary patterns. A three‐stage feature selection method is employed to provide the most discriminative features. The authors included a total of 244 patients. Training the CAD system on 108 patients (78 MR‐positive prostate cancers and 105 benign MR‐positive lesions), two validation studies were retrospectively performed on 136 patients (68 MR‐positive prostate cancers, 111 benign MR‐positive lesions, and 117 MR‐negative benign lesions). Results In distinguishing cancer from MR‐positive benign lesions, an …

Prostate cancer (PCa) is the second most common cause of cancer related deaths in men. Multiparametric MRI (mpMRI) is the most accurate imaging method for PCa detection; however, it requires the expertise of experienced radiologists leading to inconsistency across readers of varying experience. To increase inter-reader agreement and sensitivity, we developed a computer-aided detection (CAD) system that can automatically detect lesions on mpMRI that readers can use as a reference. We investigated a convolutional neural network based deep-learing (DCNN) architecture to find an improved solution for PCa detection on mpMRI. We adopted a network architecture from a state-of-the-art edge detector that takes an image as an input and produces an image probability map. Two-fold cross validation along with a receiver operating characteristic (ROC) analysis and free-response ROC (FROC) were used to …

Automated nuclei segmentation and classification are the keys to analyze and understand the cellular characteristics and functionality, supporting computer-aided digital pathology in disease diagnosis. However, the task still remains challenging due to the intrinsic variations in size, intensity, and morphology of different types of nuclei. Herein, we propose a self-guided ordinal regression neural network for simultaneous nuclear segmentation and classification that can exploit the intrinsic characteristics of nuclei and focus on highly uncertain areas during training. The proposed network formulates nuclei segmentation as an ordinal regression learning by introducing a distance decreasing discretization strategy, which stratifies nuclei in a way that inner regions forming a regular shape of nuclei are separated from outer regions forming an irregular shape. It also adopts a self-guided training strategy to adaptively adjust …

Precise Outcome prediction is crucial to providing optimal cancer care across the spectrum of solid cancers. Clinically-useful tools to predict risk of adverse events (metastases, recurrence), however, remain deficient. Here, we report an approach to predict the risk of prostate cancer recurrence, at the time of initial diagnosis, using a combination of emerging chemical imaging, a diagnostic protocol that focuses simultaneously on the tumor and its microenvironment and data analysis of frequent patterns in molecular expression. Fourier transform infrared (FT-IR) spectroscopic imaging was employed to record the structure and molecular content from tumors prostatectomy. We analyzed data from a patient cohort that is mid-grade dominant – which is the largest cohort of patients in the modern era and in whom prognostic methods are largely ineffective. Our approach outperforms the two widely used tools, Kattan …

In this paper, we present an approach of convolutional neural networks (CNNs) to identify prostate cancers. Prostate tissue specimen samples were obtained from the tissue microarrays and digitized. For each sample, epithelial nuclear seeds were identified and used to generate a nuclear seed map, i.e., only the location information of epithelial nuclei was utilized. From the nuclear seed maps, CNNs sought to learn the high-level feature representation of nuclear architecture and to detect cancers. Applying data augmentation technique, CNNs were trained on the training data set including 73 benign and 89 cancer samples and validated on the testing data set comprising 217 benign and 274 cancer samples. In detecting cancers, CNNs achieved an AUC of 0.974 (95% CI: 0.961-0.985). In comparison with the approaches of utilizing hand-crafted nuclear architecture features and the state of the art deep learning …

Trends of kidney cancer cases worldwide are expected to increase persistently and this inspires the modification of the traditional diagnosis system to respond to future challenges. Renal Cell Carcinoma (RCC) is the most common kidney cancer and responsible for 80–85% of all renal tumors. This study proposed a robust and computationally efficient fully automated Renal Cell Carcinoma Grading Network (RCCGNet) from kidney histopathology images. The proposed RCCGNet contains a shared channel residual (SCR) block which allows the network to learn feature maps associated with different versions of the input with two parallel paths. The SCR block shares the information between two different layers and operates the shared data separately by providing beneficial supplements to each other. As a part of this study, we also introduced a new dataset for the grading of RCC with five different grades. We …

Background and Objective Various digital pathology tools have been developed to aid in analyzing tissues and improving cancer pathology. The multi-resolution nature of cancer pathology, however, has not been fully analyzed and utilized. Here, we develop an automated, cooperative, and multi-resolution method for improving prostate cancer diagnosis. Methods Digitized tissue specimen images are obtained from 5 tissue microarrays (TMAs). The TMAs include 70 benign and 135 cancer samples (TMA1), 74 benign and 89 cancer samples (TMA2), 70 benign and 115 cancer samples (TMA3), 79 benign and 82 cancer samples (TMA4), and 72 benign and 86 cancer samples (TMA5). The tissue specimen images are segmented using intensity- and texture-based features. Using the segmentation results, a number of morphological features from lumens and epithelial nuclei are computed to characterize tissues at …

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge’s improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our …

A prostate computer-aided diagnosis (CAD) based on random forest to detect prostate cancer using a combination of spatial, intensity, and texture features extracted from three sequences, T2W, ADC, and B2000 images, is proposed. The random forest training considers instance-level weighting for equal treatment of small and large cancerous lesions as well as small and large prostate backgrounds. Two other approaches, based on an AutoContext pipeline intended to make better use of sequence-specific patterns, were considered. One pipeline uses random forest on individual sequences while the other uses an image filter described to produce probability map-like images. These were compared to a previously published CAD approach based on support vector machine (SVM) evaluated on the same data. The random forest, features, sampling strategy, and instance-level weighting improve prostate cancer …

Purpose We propose an approach of 3D convolutional neural network to segment the prostate in MR images. Methods A 3D deep dense multi-path convolutional neural network that follows the framework of the encoder–decoder design is proposed. The encoder is built based upon densely connected layers that learn the high-level feature representation of the prostate. The decoder interprets the features and predicts the whole prostate volume by utilizing a residual layout and grouped convolution. A set of sub-volumes of MR images, centered at the prostate, is generated and fed into the proposed network for training purpose. The performance of the proposed network is compared to previously reported approaches. Results Two independent datasets were employed to assess the proposed network. In quantitative evaluations, the proposed network achieved 95.11 and 89.01 Dice coefficients for the two datasets. The …

Purpose Temporal Enhanced Ultrasound (TeUS) has been proposed as a new paradigm for tissue characterization based on a sequence of ultrasound radio frequency (RF) data. We previously used TeUS to successfully address the problem of prostate cancer detection in the fusion biopsies. Methods In this paper, we use TeUS to address the problem of grading prostate cancer in a clinical study of 197 biopsy cores from 132 patients. Our method involves capturing high-level latent features of TeUS with a deep learning approach followed by distribution learning to cluster aggressive cancer in a biopsy core. In this hypothesis-generating study, we utilize deep learning based feature visualization as a means to obtain insight into the physical phenomenon governing the interaction of temporal ultrasound with tissue. Results Based on the evidence derived from our feature visualization, and the structure of tissue from …

Quantitative Imaging & Informatics Laboratory

교수님 소개

곽진태

연구실 소개

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

Two-Stage Residual-Guided Diffusion Framework for High-Fidelity Pathology Image Restoration

Lightweight Look-ahead Multi-context Vision Transformer

디지털 병리 이미지 분석을 위한 Mamba 기반 접근법 비교

연구실 보유 특허

대학별 입시끈

우리 연구실 모집

교수님 소개

곽진태

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

Two-Stage Residual-Guided Diffusion Framework for High-Fidelity Pathology Image Restoration

Lightweight Look-ahead Multi-context Vision Transformer

디지털 병리 이미지 분석을 위한 Mamba 기반 접근법 비교

연구실 보유 특허

최근 3년간 연구실적 높은 연구실

대학별 입시끈

우리 연구실 모집