A review of deep learning-based multiple-lesion recognition from medical images: classification, detection and segmentation

H Jiang, Z Diao, T Shi, Y Zhou, F Wang, W Hu… - Computers in Biology …, 2023 - Elsevier
Deep learning-based methods have become the dominant methodology in medical image
processing with the advancement of deep learning in natural image classification, detection …

A Comprehensive Review of Computer-Aided Models for Breast Cancer Diagnosis Using Histopathology Images

A Labrada, BD Barkana - Bioengineering, 2023 - mdpi.com
Breast cancer is the second most common cancer in women who are mainly middle-aged
and older. The American Cancer Society reported that the average risk of developing breast …

Deep learning applied for histological diagnosis of breast cancer

Y Yari, TV Nguyen, HT Nguyen - IEEE Access, 2020 - ieeexplore.ieee.org
Deep learning, as one of the currently most popular computer science research trends,
improves neural networks, which has more and deeper layers allowing higher abstraction …

DBLCNN: Dependency-based lightweight convolutional neural network for multi-classification of breast histopathology images

C Wang, W Gong, J Cheng, Y Qian - Biomedical Signal Processing and …, 2022 - Elsevier
Breast histopathology analysis is the gold standard for diagnosing breast cancer.
Convolutional neural network-based methods for breast histology image classification have …

A transformer-based network for pathology image classification

M Ding, A Qu, H Zhong, H Liang - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
Pathology image classification plays an important role in cancer diagnosis and precision
treatment. Convolutional neural network has been widely employed in pathology image …

[HTML][HTML] Unsupervised many-to-many stain translation for histological image augmentation to improve classification accuracy

M Berijanian, NS Schaadt, B Huang, J Lotz… - Journal of pathology …, 2023 - Elsevier
Background Deep learning tasks, which require large numbers of images, are widely
applied in digital pathology. This poses challenges especially for supervised tasks since …

MDAA: multi-scale and dual-adaptive attention network for breast cancer classification

W Li, H Long, X Zhan, Y Wu - Signal, Image and Video Processing, 2024 - Springer
Attention mechanism is crucial in the auxiliary diagnosis of breast cancer. However,
methods relying on a single attention mechanism may not always achieve satisfactory …

Medical image classification based on semi-supervised generative adversarial network and pseudo-labelling

K Liu, X Ning, S Liu - Sensors, 2022 - mdpi.com
Deep learning has substantially improved the state-of-the-art in object detection and image
classification. Deep learning usually requires large-scale labelled datasets to train the …

[HTML][HTML] Equilibrium Optimization-Based Ensemble CNN Framework for Breast Cancer Multiclass Classification Using Histopathological Image

Y Çetin-Kaya - Diagnostics, 2024 - pmc.ncbi.nlm.nih.gov
Background: Breast cancer is one of the most lethal cancers among women. Early detection
and proper treatment reduce mortality rates. Histopathological images provide detailed …

[PDF][PDF] Breast cancer detection from histopathology images using machine learning techniques: a bibliometric analysis

SA Joshi, AM Bongale, A Bongale - Libr. Philos. Pract, 2021 - researchgate.net
Computer aided diagnosis has become upcoming area of research over past few years.
With the advent of machine learning and especially deep learning techniques, the scenario …