Convolutional Neural Networks (CNNs) often fail to maintain their performance when they confront new test domains, which is known as the problem of domain shift. Recent studies suggest that one of the main causes of this problem is CNNs’ strong inductive bias towards image styles (i.e. textures) which are sensitive to domain changes, rather than contents (i.e. shapes). Inspired by this, we propose to reduce the intrinsic style bias of CNNs to close the gap between domains. Our Style-Agnostic Networks (SagNets) disentangle style encodings from class categories to prevent style biased predictions and focus more on the contents. Extensive experiments show that our method effectively reduces the style bias and makes the model more robust under domain shift. It achieves remarkable performance improvements in a wide range of cross-domain tasks including domain generalization, unsupervised domain adaptation, and semi-supervised domain adaptation on multiple datasets.
Hyeonseob Nam, HyunJae Lee, Jongchan Park Wonjun Yoon Donggeun Yoo Lunit Inc.
1Lunit Inc.
Learning Visual Context by Comparison
Minchul Kim et al.
ECCV (2020)
SRM: A Style-based Recalibration Module for Convolutional Neural Networks
HyunJae Lee1 et al.
ICCV (2019)
Photometric Transformer Networks and Label Adjustment for Breast Density Prediction
Jaehwan Lee1 et al.
ICCV 2019 Workshop
Learning Loss for Active Learning
Donggeun Yoo et al.
CVPR (2019)
PseudoEdgeNet: Nuclei Segmentation only with Point Annotations
Inwan Yoo et al.
MICCAI (2019)
External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms
Mattie Salim, MD et al.
JAMA Oncology (2020)
Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks
Hyeonseob Nam et al.
NeurIPS (2018)
Distort-and-Recover: Color Enhancement Using Deep Reinforcement Learning
Jongchan Park et al.
CVPR (2018)
CBAM: Convolutional Block Attention Module
Jongchan Park1 et al.
ECCV (2018)
BAM: Bottleneck Attention Module
Jongchan Park et al.
BMVC (2018)
A Robust and Effective Approach Towards Accurate Metastasis Detection and pN-stage Classification in Breast Cancer
Byungjae Lee et al.
MICCAI (2018)
Keep and Learn: Continual Learning by Constraining the Latent Space for Knowledge Preservation in Neural Networks
Hyo-Eun Kim et al.
MICCAI (2018)
Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks
Sangheum Hwang et al.
MICCAI 2017 DLMIA Workshop
A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology
Kyunghyun Paeng et al.
MICCAI 2017 DLMIA Workshop
Transferring Knowledge to Smaller Network With Class-Distance Loss
Seungwook Kim et al.
ICLR 2017 Workshop
Self-Transfer Learning for Fully Weakly Supervised Object Localization
Sangheum Hwang et al.
MICCAI (2016)
Pixel-Level Domain Transfer
Donggeun Yoo et al.
ECCV (2016)
AttentionNet: Aggregating Weak Directions for Accurate Object Detection
Donggeun Yoo et al.
ICCV (2015)
Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study
Hyo-Eun Kim, PhD et al.
Lancet Digital Health (2020)
Development and Validation of a Deep Learning–based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs
Sunggyun Park et al.
Radiology (2018)
Clinical Validation of a Deep Learning Algorithm for Detection of Pneumonia on Chest Radiographs in Emergency Department Patients with Acute Febrile Respiratory Illness
Jae Hyun Kim et al.
Journal of Clinical Medicine (2020)
Undetected Lung Cancer at Posteroanterior Chest Radiography: Potential Role of a Deep Learning–based Detection Algorithm
Ju Gang Nam et al.
Radiology (2020)
Deep-learning algorithms for the interpretation of chest radiographs to aid in the triage of COVID-19 patients: A multicenter retrospective study
Se Bum Jang et al.
PLOS ONE (2020)
Validation of a Deep Learning Algorithm for the Detection of Malignant Pulmonary Nodules in Chest Radiographs
Hyunsuk Yoo et al.
JAMA Network Open (2020)
Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study
Mattie Salim, MD et al.
Lancet Digital Health (2020)
Deep-learning Based Automated Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs: Diagnostic Performance in Systematic Screening of Asymptomatic Individuals
Jong Hyuk Lee et al.
European Radiology (2020)
Performance of a Deep-learning Algorithm Compared to Radiologic Interpretation for Lung Cancer Detection on Chest Radiographs in a Health Screening Population
Jong Hyuk Lee et al.
Radiology (2020)
Implementation of a Deep Learning-Based Computer-Aided Detection System for the Interpretation of Chest Radiographs in Patients Suspected for COVID-19
Eui Jin Hwang, MD, PhD et al.
Korean Journal of Radiology (2020)
Deep Learning–based Automatic Detection Algorithm for Reducing Overlooked Lung Cancers on Chest Radiographs
Sowon Jang et al.
Radiology (2020)
Automated identification of chest radiographs with referable abnormality with deep learning: need for recalibration
Eui Jin Hwang et al.
European Radiology (2020)
Deep learning algorithm for surveillance of pneumothorax after lung biopsy: a multicenter diagnostic cohort study
Eui Jin Hwang et al.
European Radiology (2020)
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms
Thomas Schaffter, PhD et al.
JAMA Network Open (2020)
Test-retest reproducibility of a deep learning–based automatic detection algorithm for the chest radiograph
Hyungjin Kim et al.
European Radiology (2020)
Deep Learning for Chest Radiograph Diagnosis in the Emergency Department
Eui Jin Hwang et al.
Radiology (2019)
Development and Validation of a Deep Learning–Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs
Eui Jin Hwang et al.
JAMA Network Open (2019)
Development and Validation of a Deep Learning–based Automatic Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs
Eui Jin Hwang et al.
Clinical Infectious Diseases (2018)
Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study
Eun-Kyung Kim et al.
Scientific Reports (2018)
Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs
Ju Gang Nam et al.
European Respiratory Journal (2020)
Abstract: Deep learning-based predictive biomarker for adjuvant chemotherapy in early-stage hormone receptor-positive breast cancer
Soo Youn Cho et al.
AACR 2019
Abstract: Pan-cancer analysis of tumor microenvironment using deep learning-based cancer stroma and immune profiling in H&E images
Kyunghyun Paeng et al.
AACR 2019
Abstract: Deep learning-based predictive biomarker for immune checkpoint inhibitor response in metastatic non-small cell lung cancer
Sehhoon Park et al.
ASCO 2019
Abstract: Comprehensive deep learning analysis of H&E tissue phenomics reveals distinct immune landscape and transcriptomic enrichment profile among immune inflamed, excluded and desert subtypes...
Jonghanne Park et al.
AACR 2020
Abstract: Deep-learning analysis of H&E images to define three immune phenotypes to reveal loss-of-target in excluded immune cells as a novel resistance mechanism of immune checkpoint inhibitor...
Sehhoon Park et al.
ASCO 2020
Abstract: Deep learning-based immune phenotype analysis reveals distinct resistance pattern of immune checkpoint inhibitor in non-small cell lung cancer
Chan-Young Ock et al.
ASCO 2020
Application of artificial intelligence–based computer-assisted diagnosis on synthetic mammograms from breast tomosynthesis: comparison with digital mammograms
Si Eun Lee et al.
European Radiology (2021)
Performance of a deep-learning algorithm for referable thoracic abnormalities on chest radiographs: A multicenter study of a health screening cohort
Eun Young Kim et al.
PLOS ONE (2021)
AI-based improvement in lung cancer detection on chest radiographs: results of a multi-reader study in NLST dataset
Hyunsuk Yoo et al.
European Radiology (2021)
COVID-19 pneumonia on chest X-rays: Performance of a deep learning-based computer-aided detection system
Eui Jin Hwang et al.
PLOS ONE (2021)
유방촬영의 위양성 판정에 관한 전통적 진단보조프로그램과 인공지능 기반 진단보조프로그램의 비교
이시은 et al.
대한유방검진의학회지
인공지능 기반 컴퓨터 보조진단을 이용한 선별 유방촬영술에서의 간격암에 대한 후향 분석
김연수 et al.
대한유방검진의학회지
Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge
Mitko Veta et al.
Medical Image Analysis (2019)
Accuracy and efficiency of an artificial intelligence tool when counting breast mitoses
Liron Pantanowitz et al.
Diagnostic Pathology (2020)
Abstract: Artificial intelligence-powered spatial analysis of tumor infiltrating lymphocytes (TIL) to reflect target gene expressions of novel immuno-oncology agents.
Chan-Young Ock et al.
ASCO (2021)
Abstract: Distinct subset of immune cells assessed by multiplex immunohistochemistry correlates with immune phenotype classified by ...
Yoon-La Choi et al.
ASCO 2021
Abstract : Pathologic validation of artificial intelligence-powered prediction of combined positive score of PD-L1 immunohistochemistry in urothelial carcinoma.
Jeong Hwan Park et al.
ASCO 2021
Abstract : Clinical performance of artificial intelligence-powered annotation of tumor cell PD-L1 expression for treatment of immune-checkpoint inhibitor (ICI) ...
Hyojin Kim et al.
ASCO 2021
Abstract : Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes predicts survival after immune checkpoint inhibitor therapy across multiple cancer types.
Jeanne Shen et al.
ASCO (2021)
Abstract : Deep learning based radiomic biomarker for predicting the presence of high-grade histologic patterns in lung adenocarcinoma
Yeonu Choi et al.
AACR 2021
Abstract : Artificial intelligence-powered tissue analysis reveals distinct tumor-infiltrating lymphocyte profile as a potential biomarker of molecular subtypes in endometrial cancer
Hyojin Kim et al.
AACR 2021
Abstract : Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes reveals distinct genomic profile of immune excluded phenotype in pan-carcinoma
Chan-Young Ock et al.
AACR 2021
Deep learning from HE slides predicts the clinical benefit from adjuvant chemotherapy in hormone receptor-positive breast cancer patients
Soo Youn Cho et al.
Scientific Reports (2021)
Deep Learning for Detection of Pulmonary Metastasis on Chest Radiographs
Eui JEui Jin Hwang et al.
Radiology (2021)
Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems
Zhi Zhen Qin et al.
Scientific Reports
Mammographic Surveillance After Breast Conserving Therapy: Impact of Digital Breast Tomosynthesis and Artificial Intelligence-Based Computer-Aided Detection
Jung Hyun Yoon et al.
American Journal of Roentgenology
Two-Phase Learning for Weakly Supervised Object Localization
Dahun Kim et al.
ICCV (2017)
Weakly-Supervised Physically Unconstrained Gaze Estimation
Rakshit Kothari et al.
CVPR (2021)
Polygonal Point Set Tracking
Gunhee Nam et al.
CVPR (2021)
Multi-scale Pyramid Pooling for Deep Convolutional Representation
Donggeun Yoo et al.
CVPR Workshop (2015)
Abstract : Interim results of phase I dose escalation study of YBL-006: A novel anti-PD-1 monoclonal antibody in advanced solid tumors
K. Lee et al.
ESMO (2021)
Abstract : Assistance with an artificial intelligence-powered PD-L1 analyzer reduces interobserver variation in pathologic reading of tumor proportion score in non-small cell lung cancer
S. Choi et al.
ESMO (2021)
Abstract : AI-powered whole-slide image analysis of tumor-infiltrating lymphocytes for prediction of prognosis in colorectal cancer
C. Park et al.
ESMO (2021)
Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Diagnosis for Breast Microcalcification on Mammography
Yoon Ah Do et al.
Diagnostics (2021)
Can artificial intelligence replace ultrasound as a complementary tool to mammogram for the diagnosis of the breast cancer?
Sahar Mansour et al.
The British Journal of Radiology
Deep Learning Analysis of CT Images Reveals High-Grade Pathological Features to Predict Survival in Lung Adenocarcinoma
Yeonu Choi et al.
Cancers (2021)
Deep learning computer-aided detection system for pneumonia in febrile neutropenia patients: a diagnostic cohort study
Eui Jin Hwang et al.
BMC (2021)
Independent evaluation of 12 artifcial intelligence solutions for the detection of tuberculosis
Andrew J. Codlin et al.
Scientific Reports (2021)
Effect of deep learning-based assistive technology use on chest radiograph interpretation by emergency department physicians: a prospective interventional simulation-based study
Ji Hoon Kim et al.
BMC (2021)
Diagnostic effect of artificial intelligence solution for referable thoracic abnormalities on chest radiography: a multicenter respiratory outpatient diagnostic cohort study
Kwang Nam Jin et al.
European Radiology (2022)
Evaluation of a deep learning-based computer-aided detection algorithm on chest radiographs : Case–control study
Choi, Soo Yun MS et al.
Medicine (2021)