Breast Cancer Image

Breast cancer image analysis focuses on developing automated systems for accurate and efficient diagnosis and prognosis prediction using digital pathology images. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs), vision transformers (ViTs), and ensemble methods to classify images, segment regions of interest, and predict molecular markers like HER2 status from hematoxylin and eosin (H&E) stained slides. These advancements aim to improve diagnostic accuracy, reduce inter-observer variability, and streamline workflows, ultimately leading to better patient care and more efficient use of pathologist time. Furthermore, research is actively exploring methods to address data imbalance, improve model interpretability, and ensure robustness across different staining protocols and imaging devices.

Papers

September 24, 2024

An ensemble framework approach of hybrid Quantum convolutional neural networks for classification of breast cancer images
Dibyasree Guha, Shyamali Mitra, Somenath Kuiry, Nibaran Das
Convolutional Neural Network Classification Code Quantum Physic Ensemble Approach Classical Neural Network Quantum Model Quantum Classical Neural Network Hybrid Quantum Breast Cancer Image

August 28, 2024

PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification
Alek Fröhlich, Thiago Ramos, Gustavo Cabello, Isabela Buzatto, Rafael Izbicki, Daniel Tiezzi
Conformal Prediction Coverage Criterion Breast Cancer Risk BI Rad Breast Cancer Image Lesion Age

April 1, 2024

Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling
Sahan Yoruc Selcuk, Xilin Yang, Bijie Bai, Yijie Zhang, Yuzhu Li, Musa Aydin, Aras Firat Unal, Aditya Gomatam, Zhen Guo, Darrow Morgan Angus, Goren Kolodney, Karine Atlan, Tal Keidar Haran, Nir Pillar, Aydogan Ozcan
Deep Learning Virtual Immunohistochemistry Tissue Phenotyping Slice Sampling Breast Cancer Image Human Epidermal Growth Factor Receptor

March 29, 2024

Segmentation, Classification and Interpretation of Breast Cancer Medical Images using Human-in-the-Loop Machine Learning
David Vázquez-Lema, Eduardo Mosqueira-Rey, Elena Hernández-Pereira, Carlos Fernández-Lozano, Fernando Seara-Romera, Jorge Pombo-Otero
Classification Code Segmentation Based Approach Human in the Loop Abstract Interpretation Training Machine Learning Model Breast Cancer Image

November 2, 2023

Hierarchical Classification System for Breast Cancer Specimen Report (HCSBC) -- an end-to-end model for characterizing severity and diagnosis
Thiago Santos, Harish Kamath, Christopher R. McAdams, Mary S. Newell, Marina Mosunjac, Gabriela Oprea-Ilies, Geoffrey Smith, Constance Lehman, Judy Gichoya, Imon Banerjee, Hari Trivedi
NLP Model Breast Cancer Hierarchical Classification Severity Prediction End to End Model Breast Cancer Histopathological Image Classification Breast Cancer Image

October 15, 2023

Class-Specific Data Augmentation: Bridging the Imbalance in Multiclass Breast Cancer Classification
Kanan Mahammadli, Abdullah Burkan Bereketoglu, Ayse Gul Kabakci
Data Augmentation Breast Cancer Multiclass Classification Group Imbalance Stain Normalization Breast Cancer Image

August 3, 2023

Predicting Ki67, ER, PR, and HER2 Statuses from H&E-stained Breast Cancer Images
Amir Akbarnejad, Nilanjan Ray, Penny J. Barnes, Gilbert Bigras
Digital Pathology Immunohistochemistry Image Emergency Department Breast Cancer Image Ki 67 IHC Stained Image

January 30, 2023

Standardized CycleGAN training for unsupervised stain adaptation in invasive carcinoma classification for breast histopathology
Nicolas Nerrienet, Rémy Peyret, Marie Sockeel, Stéphane Sockeel
CycleGAN Model Cancer Classification Stain Transformation Stain Invariant Breast Cancer Image Stain Augmentation Stain Translation

April 18, 2022

Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology
Yuchao Zheng, Chen Li, Xiaomin Zhou, Haoyuan Chen, Hao Xu, Yixin Li, Haiqing Zhang, Xiaoyan Li, Hongzan Sun, Xinyu Huang, Marcin Grzegorzek
Transfer Learning Application Proficiency Ensemble Learning Binary Classification Deep Ensemble Ensemble Neural Network Breast Cancer Image Image Level Classification

Breast Cancer Image

Papers

An ensemble framework approach of hybrid Quantum convolutional neural networks for classification of breast cancer images

PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification

Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling

Segmentation, Classification and Interpretation of Breast Cancer Medical Images using Human-in-the-Loop Machine Learning

Hierarchical Classification System for Breast Cancer Specimen Report (HCSBC) -- an end-to-end model for characterizing severity and diagnosis

Class-Specific Data Augmentation: Bridging the Imbalance in Multiclass Breast Cancer Classification

Predicting Ki67, ER, PR, and HER2 Statuses from H&E-stained Breast Cancer Images

Standardized CycleGAN training for unsupervised stain adaptation in invasive carcinoma classification for breast histopathology

Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology