Breast Cancer Classification

Breast cancer classification research aims to develop accurate and efficient methods for distinguishing between different types of breast cancer using various imaging modalities (mammography, ultrasound, histopathology) and genomic data. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs), transformers, and hybrid architectures like CNN-Transformer combinations, often enhanced by ensemble methods and transfer learning techniques to improve classification accuracy and address data limitations. These advancements hold significant potential for improving early detection, diagnosis, and personalized treatment strategies, ultimately impacting patient outcomes and clinical practice.

Papers

October 14, 2024

Performance Evaluation of Deep Learning and Transformer Models Using Multimodal Data for Breast Cancer Classification
Sadam Hussain, Mansoor Ali, Usman Naseem, Beatriz Alejandra Bosques Palomo, Mario Alexis Monsivais Molina, Jorge Alberto Garza Abdala, Daly Betzabeth Avendano Avalos, Servando Cardona-Huerta, T. Aaron Gulliver, Jose Gerardo Tamez Pena
Deep Learning Transformer Model Multimodal Data Multimodal Dataset Performance Evaluation Breast Cancer Classification Digital Mammogram

October 4, 2024

Comparative Analysis and Ensemble Enhancement of Leading CNN Architectures for Breast Cancer Classification
Gary Murphy, Raghubir Singh
Convolutional Neural Network Comparative Study Medical Image Datasets Breast Cancer Histopathology Image Convolutional Neural Network Architecture Breast Cancer Classification Ensemble Post Processing

September 5, 2024

Deep Transfer Learning for Breast Cancer Classification
Prudence Djagba, J. K. Buwa Mbouobda
Vision Transformer Deep Learning Model Deep Transfer Learning Breast Cancer Classification

August 29, 2024

Comparative Analysis of Transfer Learning Models for Breast Cancer Classification
Sania Eskandari, Ali Eslamian, Qiang Cheng
Deep Learning Model Transfer Learning Comparative Study Histopathological Image Breast Cancer Classification

August 23, 2024

Interpretable breast cancer classification using CNNs on mammographic images
Ann-Kristin Balve, Peter Hendrix
Convolutional Neural Network Breast Cancer Image Analysis Mammogram Classification Breast Cancer Classification Post Hoc Interpretability

August 20, 2024

Breast tumor classification based on self-supervised contrastive learning from ultrasound videos
Yunxin Tang, Siyuan Tang, Jian Zhang, Hao Chen
Supervised ImageNet Self Supervised Contrastive Learning Breast Ultrasound Breast Cancer Classification Ultrasound Video

July 27, 2024

AResNet-ViT: A Hybrid CNN-Transformer Network for Benign and Malignant Breast Nodule Classification in Ultrasound Images
Xin Zhao, Qianqian Zhu, Jialing Wu
Ultrasound Image Multi Head Self Attention Global Feature Breast Ultrasound Cancer Classification Hybrid CNN Transformer Breast Cancer Classification Global Image Feature Benign Input

July 15, 2024

Stacking-Enhanced Bagging Ensemble Learning for Breast Cancer Classification with CNN
Peihceng Wu, Runze Ma, Teoh Teik Toe
Ensemble Learning CNN Network Breast Cancer Classification Ensemble Bagging

July 1, 2024

Deep learning for automated detection of breast cancer in deep ultraviolet fluorescence images with diffusion probabilistic model
Sepehr Salem Ghahfarokhi, Tyrell To, Julie Jorns, Tina Yen, Bing Yu, Dong Hye Ye
Deep Learning Data Detection Probabilistic Model Breast Cancer Breast Cancer Detection Breast Cancer Classification Fluorescence Spectroscopy

April 9, 2024

Leveraging Latents for Efficient Thermography Classification and Segmentation
Tamir Shor, Chaim Baskin, Alex Bronstein
Segmentation Based Approach Breast Cancer Classification Breast Tumor Segmentation Situated Latents

April 6, 2024

Predictive Modeling for Breast Cancer Classification in the Context of Bangladeshi Patients: A Supervised Machine Learning Approach with Explainable AI
Taminul Islam, Md. Alif Sheakh, Mst. Sazia Tahosin, Most. Hasna Hena, Shopnil Akash, Yousef A. Bin Jardan, Gezahign Fentahun Wondmie, Hiba-Allah Nafidi, Mohammed Bourhia
Machine Learning Explainable AI Decision Tree Predictive Model Breast Cancer Logistic Regression Supervised Machine Learning Breast Cancer Classification

April 3, 2024

A Novel Approach to Breast Cancer Histopathological Image Classification Using Cross-Colour Space Feature Fusion and Quantum-Classical Stack Ensemble Method
Sambit Mallick, Snigdha Paul, Anindya Sen
Novel Approach Feature Fusion Breast Cancer Classification Color Space Breast Cancer Diagnosis Stacking Ensemble Breast Cancer Histopathological Image Classification

March 17, 2024

A lightweight deep learning pipeline with DRDA-Net and MobileNet for breast cancer classification
Mahdie Ahmadi, Nader Karimi, Shadrokh Samavi
Deep Learning Histopathological Image Breast Cancer Classification L MobileNet Residual Attention Lightweight Deep

March 14, 2024

Breast Cancer Classification Using Gradient Boosting Algorithms Focusing on Reducing the False Negative and SHAP for Explainability
João Manoel Herrera Pinheiro, Marcelo Becker
High Explainability Breast Cancer Gradient Boosting Area Under the ROC Curve Breast Cancer Classification Fusion SHAP False Negative Cancer Prediction

February 29, 2024

Towards Out-of-Distribution Detection for breast cancer classification in Point-of-Care Ultrasound Imaging
Jennie Karlsson, Marisa Wodrich, Niels Christian Overgaard, Freja Sahlin, Kristina Lång, Anders Heyden, Ida Arvidsson
Distribution Detection Deep Ensemble Softmax Function Ensemble Method Distribution Sample Breast Cancer Classification Point of Care

February 26, 2024

MV-Swin-T: Mammogram Classification with Multi-view Swin Transformer
Sushmita Sarker, Prithul Sarker, George Bebis, Alireza Tavakkoli
Swin Transformer Multiple View Mammogram Classification Breast Cancer Classification Swin Unet Multi View Network

January 25, 2024

A comparative study of zero-shot inference with large language models and supervised modeling in breast cancer pathology classification
Madhumita Sushil, Travis Zack, Divneet Mandair, Zhiwei Zheng, Ahmed Wali, Yan-Ning Yu, Yuwei Quan, Atul J. Butte
Large Language Model Zero Shot Comparative Study BERT Model Breast Cancer Classification Large Scale Annotated Label Model Clinical NLP

December 5, 2023

Enhanced Breast Cancer Tumor Classification using MobileNetV2: A Detailed Exploration on Image Intensity, Error Mitigation, and Streamlit-driven Real-time Deployment
Aaditya Surya, Aditya Shah, Jarnell Kabore, Subash Sasikumar
Breast Cancer Ultrasound Image Breast Cancer Classification Error Mitigation L MobileNet Real Time Deployment Image Intensity

November 23, 2023

MedISure: Towards Assuring Machine Learning-based Medical Image Classifiers using Mixup Boundary Analysis
Adam Byfield, William Poulett, Ben Wallace, Anusha Jose, Shatakshi Tyagi, Smita Shembekar, Adnan Qayyum, Junaid Qadir, Muhammad Bilal
Machine Learning Machine Learning Model Medical Image Classification Mixup Method Breast Cancer Classification

November 22, 2023

Breast Cancer classification by adaptive weighted average ensemble of previously trained models
Mosab S. M. Farea, zhe chen
Full Model Adaptive Importance Histopathology Image Ensemble Model Early Detection Breast Cancer Classification Adaptive Ensemble Weighted Ensemble Ensemble Averaging

Breast Cancer Classification

Papers

Performance Evaluation of Deep Learning and Transformer Models Using Multimodal Data for Breast Cancer Classification

Comparative Analysis and Ensemble Enhancement of Leading CNN Architectures for Breast Cancer Classification

Deep Transfer Learning for Breast Cancer Classification

Comparative Analysis of Transfer Learning Models for Breast Cancer Classification

Interpretable breast cancer classification using CNNs on mammographic images

Breast tumor classification based on self-supervised contrastive learning from ultrasound videos

AResNet-ViT: A Hybrid CNN-Transformer Network for Benign and Malignant Breast Nodule Classification in Ultrasound Images

Stacking-Enhanced Bagging Ensemble Learning for Breast Cancer Classification with CNN

Deep learning for automated detection of breast cancer in deep ultraviolet fluorescence images with diffusion probabilistic model

Leveraging Latents for Efficient Thermography Classification and Segmentation

Predictive Modeling for Breast Cancer Classification in the Context of Bangladeshi Patients: A Supervised Machine Learning Approach with Explainable AI

A Novel Approach to Breast Cancer Histopathological Image Classification Using Cross-Colour Space Feature Fusion and Quantum-Classical Stack Ensemble Method

A lightweight deep learning pipeline with DRDA-Net and MobileNet for breast cancer classification

Breast Cancer Classification Using Gradient Boosting Algorithms Focusing on Reducing the False Negative and SHAP for Explainability

Towards Out-of-Distribution Detection for breast cancer classification in Point-of-Care Ultrasound Imaging

MV-Swin-T: Mammogram Classification with Multi-view Swin Transformer

A comparative study of zero-shot inference with large language models and supervised modeling in breast cancer pathology classification

Enhanced Breast Cancer Tumor Classification using MobileNetV2: A Detailed Exploration on Image Intensity, Error Mitigation, and Streamlit-driven Real-time Deployment

MedISure: Towards Assuring Machine Learning-based Medical Image Classifiers using Mixup Boundary Analysis

Breast Cancer classification by adaptive weighted average ensemble of previously trained models