A brief survey on breast cancer diagnostic with deep learning schemes using multi-image modalities
Patients with breast cancer are prone to serious health-related complications with higher
mortality. The primary reason might be a misinterpretation of radiologists in recognizing …
mortality. The primary reason might be a misinterpretation of radiologists in recognizing …
Recent trends in computer assisted diagnosis (CAD) system for breast cancer diagnosis using histopathological images
Breast cancer is one of the common type of cancer in females across the world. An early
detection and diagnosis of breast cancer may reduce the mortality rate to a great extent. To …
detection and diagnosis of breast cancer may reduce the mortality rate to a great extent. To …
Automatic classification of breast cancer histopathological images based on deep feature fusion and enhanced routing
P Wang, J Wang, Y Li, P Li, L Li, M Jiang - Biomedical Signal Processing …, 2021 - Elsevier
Automatic classification of breast cancer histopathological images is of great application
value in breast cancer diagnosis. Convolutional neural network (CNN) usually highlights …
value in breast cancer diagnosis. Convolutional neural network (CNN) usually highlights …
DigestPath: A benchmark dataset with challenge review for the pathological detection and segmentation of digestive-system
Examination of pathological images is the golden standard for diagnosing and screening
many kinds of cancers. Multiple datasets, benchmarks, and challenges have been released …
many kinds of cancers. Multiple datasets, benchmarks, and challenges have been released …
Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine
W Yang, Y Si, D Wang, B Guo - Computers in biology and medicine, 2018 - Elsevier
Electrocardiogram (ECG) classification is an important process in identifying arrhythmia, and
neural network models have been widely used in this field. However, these models are often …
neural network models have been widely used in this field. However, these models are often …
Dense deconvolutional network for skin lesion segmentation
Automatic delineation of skin lesion contours from dermoscopy images is a basic step in the
process of diagnosis and treatment of skin lesions. However, it is a challenging task due to …
process of diagnosis and treatment of skin lesions. However, it is a challenging task due to …
A tour of unsupervised deep learning for medical image analysis
Background: Interpretation of medical images for the diagnosis and treatment of complex
diseases from high-dimensional and heterogeneous data remains a key challenge in …
diseases from high-dimensional and heterogeneous data remains a key challenge in …
Deep transfer learning for modality classification of medical images
Medical images are valuable for clinical diagnosis and decision making. Image modality is
an important primary step, as it is capable of aiding clinicians to access required medical …
an important primary step, as it is capable of aiding clinicians to access required medical …
A visually interpretable deep learning framework for histopathological image-based skin cancer diagnosis
Owing to the high incidence rate and the severe impact of skin cancer, the precise diagnosis
of malignant skin tumors is a significant goal, especially considering treatment is normally …
of malignant skin tumors is a significant goal, especially considering treatment is normally …
Ultrasound standard plane detection using a composite neural network framework
Ultrasound (US) imaging is a widely used screening tool for obstetric examination and
diagnosis. Accurate acquisition of fetal standard planes with key anatomical structures is …
diagnosis. Accurate acquisition of fetal standard planes with key anatomical structures is …