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.
Learning Visual Context by Comparison
SRM: A Style-based Recalibration Module for Convolutional Neural Networks
Photometric Transformer Networks and Label Adjustment for Breast Density Prediction
Learning Loss for Active Learning
PseudoEdgeNet: Nuclei Segmentation only with Point Annotations
External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms
Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks
Distort-and-Recover: Color Enhancement Using Deep Reinforcement Learning
CBAM: Convolutional Block Attention Module
BAM: Bottleneck Attention Module
A Robust and Effective Approach Towards Accurate Metastasis Detection and pN-stage Classification in Breast Cancer
Keep and Learn: Continual Learning by Constraining the Latent Space for Knowledge Preservation in Neural Networks
Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks
A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology
Transferring Knowledge to Smaller Network With Class-Distance Loss
Self-Transfer Learning for Fully Weakly Supervised Object Localization
Pixel-Level Domain Transfer
AttentionNet: Aggregating Weak Directions for Accurate Object Detection
Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study
Development and Validation of a Deep Learning–based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs
Clinical Validation of a Deep Learning Algorithm for Detection of Pneumonia on Chest Radiographs in Emergency Department Patients with Acute Febrile Respiratory Illness
Undetected Lung Cancer at Posteroanterior Chest Radiography: Potential Role of a Deep Learning–based Detection Algorithm
Deep-learning algorithms for the interpretation of chest radiographs to aid in the triage of COVID-19 patients: A multicenter retrospective study
Validation of a Deep Learning Algorithm for the Detection of Malignant Pulmonary Nodules in Chest Radiographs
Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study
Deep-learning Based Automated Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs: Diagnostic Performance in Systematic Screening of Asymptomatic Individuals
Performance of a Deep-learning Algorithm Compared to Radiologic Interpretation for Lung Cancer Detection on Chest Radiographs in a Health Screening Population
Implementation of a Deep Learning-Based Computer-Aided Detection System for the Interpretation of Chest Radiographs in Patients Suspected for COVID-19
Deep Learning–based Automatic Detection Algorithm for Reducing Overlooked Lung Cancers on Chest Radiographs
Automated identification of chest radiographs with referable abnormality with deep learning: need for recalibration
Deep learning algorithm for surveillance of pneumothorax after lung biopsy: a multicenter diagnostic cohort study
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms
Test-retest reproducibility of a deep learning–based automatic detection algorithm for the chest radiograph
Deep Learning for Chest Radiograph Diagnosis in the Emergency Department
Development and Validation of a Deep Learning–Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs
Development and Validation of a Deep Learning–based Automatic Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs
Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study
Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs
Abstract: Deep learning-based predictive biomarker for adjuvant chemotherapy in early-stage hormone receptor-positive breast cancer
Abstract: Pan-cancer analysis of tumor microenvironment using deep learning-based cancer stroma and immune profiling in H&E images
Abstract: Deep learning-based predictive biomarker for immune checkpoint inhibitor response in metastatic non-small cell lung cancer
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...
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...
Abstract: Deep learning-based immune phenotype analysis reveals distinct resistance pattern of immune checkpoint inhibitor in non-small cell lung cancer
Application of artificial intelligence–based computer-assisted diagnosis on synthetic mammograms from breast tomosynthesis: comparison with digital mammograms
Performance of a deep-learning algorithm for referable thoracic abnormalities on chest radiographs: A multicenter study of a health screening cohort
AI-based improvement in lung cancer detection on chest radiographs: results of a multi-reader study in NLST dataset
COVID-19 pneumonia on chest X-rays: Performance of a deep learning-based computer-aided detection system
유방촬영의 위양성 판정에 관한 전통적 진단보조프로그램과 인공지능 기반 진단보조프로그램의 비교
인공지능 기반 컴퓨터 보조진단을 이용한 선별 유방촬영술에서의 간격암에 대한 후향 분석
Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge
Accuracy and efficiency of an artificial intelligence tool when counting breast mitoses
Abstract: Artificial intelligence-powered spatial analysis of tumor infiltrating lymphocytes (TIL) to reflect target gene expressions of novel immuno-oncology agents.
Abstract: Distinct subset of immune cells assessed by multiplex immunohistochemistry correlates with immune phenotype classified by ...
Abstract : Pathologic validation of artificial intelligence-powered prediction of combined positive score of PD-L1 immunohistochemistry in urothelial carcinoma.
Abstract : Clinical performance of artificial intelligence-powered annotation of tumor cell PD-L1 expression for treatment of immune-checkpoint inhibitor (ICI) ...
Abstract : Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes predicts survival after immune checkpoint inhibitor therapy across multiple cancer types.
Abstract : Deep learning based radiomic biomarker for predicting the presence of high-grade histologic patterns in lung adenocarcinoma
Abstract : Artificial intelligence-powered tissue analysis reveals distinct tumor-infiltrating lymphocyte profile as a potential biomarker of molecular subtypes in endometrial cancer
Abstract : Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes reveals distinct genomic profile of immune excluded phenotype in pan-carcinoma
Deep learning from HE slides predicts the clinical benefit from adjuvant chemotherapy in hormone receptor-positive breast cancer patients
Deep Learning for Detection of Pulmonary Metastasis on Chest Radiographs
Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems
Mammographic Surveillance After Breast Conserving Therapy: Impact of Digital Breast Tomosynthesis and Artificial Intelligence-Based Computer-Aided Detection
Two-Phase Learning for Weakly Supervised Object Localization
Weakly-Supervised Physically Unconstrained Gaze Estimation
Polygonal Point Set Tracking
Multi-scale Pyramid Pooling for Deep Convolutional Representation
Abstract : Interim results of phase I dose escalation study of YBL-006: A novel anti-PD-1 monoclonal antibody in advanced solid tumors
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
Abstract : AI-powered whole-slide image analysis of tumor-infiltrating lymphocytes for prediction of prognosis in colorectal cancer
Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Diagnosis for Breast Microcalcification on Mammography
Can artificial intelligence replace ultrasound as a complementary tool to mammogram for the diagnosis of the breast cancer?
Deep Learning Analysis of CT Images Reveals High-Grade Pathological Features to Predict Survival in Lung Adenocarcinoma
Deep learning computer-aided detection system for pneumonia in febrile neutropenia patients: a diagnostic cohort study
Independent evaluation of 12 artifcial intelligence solutions for the detection of tuberculosis
Effect of deep learning-based assistive technology use on chest radiograph interpretation by emergency department physicians: a prospective interventional simulation-based study
Diagnostic effect of artificial intelligence solution for referable thoracic abnormalities on chest radiography: a multicenter respiratory outpatient diagnostic cohort study
Evaluation of a deep learning-based computer-aided detection algorithm on chest radiographs : Case–control study