Breast Cancer Detection
28 papers with code • 4 benchmarks • 7 datasets
Benchmarks
These leaderboards are used to track progress in Breast Cancer Detection
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Datasets
BRACS
CBIS-DDSM
BCI
BreakHis
BreastClassifications4
Latest papers with no code
Breast Cancer Image Classification Method Based on Deep Transfer Learning
To address the issues of limited samples, time-consuming feature design, and low accuracy in detection and classification of breast cancer pathological images, a breast cancer image classification model algorithm combining deep learning and transfer learning is proposed.
BreastRegNet: A Deep Learning Framework for Registration of Breast Faxitron and Histopathology Images
Training such automated detection models require accurate ground truth labels on ex-vivo radiology images, which can be acquired through registering Faxitron and histopathology images and mapping the extent of cancer from histopathology to x-ray images.
Distribution-based Low-rank Embedding
These findings suggest that JSE and Weibull embedding techniques substantially help preserve crucial thermal patterns as a biomarker leading to improved CBE and enabling the very early detection of breast cancer.
Breast Cancer Detection Using Deep Learning Technique Based On Ultrasound Image
Breast cancer ranks as the most prevalent form of cancer diagnosed in women, and diagnosis faces several challenges, a change in the size, shape and appearance of breasts, dense breast tissue, lumps or thickening in the breast especially if in only one breast, lumps and nodules in the breast.
GroupMixer: Patch-based Group Convolutional Neural Network for Breast Cancer Detection from Histopathological Images
In this paper, we borrowed the previously introduced idea of integrating a fully Convolutional Neural Network architecture with Patch Embedding operation and presented an efficient CNN architecture for breast cancer malignancy detection from histopathological images.
TransReg: Cross-transformer as auto-registration module for multi-view mammogram mass detection
Screening mammography is the most widely used method for early breast cancer detection, significantly reducing mortality rates.
Computational Pathology at Health System Scale -- Self-Supervised Foundation Models from Three Billion Images
Recent breakthroughs in self-supervised learning have enabled the use of large unlabeled datasets to train visual foundation models that can generalize to a variety of downstream tasks.
Post-Hoc Explainability of BI-RADS Descriptors in a Multi-task Framework for Breast Cancer Detection and Segmentation
Despite recent medical advancements, breast cancer remains one of the most prevalent and deadly diseases among women.
Breast Cancer Detection and Diagnosis: A comparative study of state-of-the-arts deep learning architectures
By improving the workflow of pathologists, these AI models have the potential to enhance the detection and diagnosis of breast cancer.
Exploring Regions of Interest: Visualizing Histological Image Classification for Breast Cancer using Deep Learning
Additionally, we experimented with three pixel selection methods: Bins, K-means, and MeanShift.
