A survey on deep learning in medical image analysis
Deep learning algorithms, in particular convolutional networks, have rapidly become a
methodology of choice for analyzing medical images. This paper reviews the major deep …
methodology of choice for analyzing medical images. This paper reviews the major deep …
Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges
Breast cancer is a common and fatal disease among women worldwide. Therefore, the early
and precise diagnosis of breast cancer plays a pivotal role to improve the prognosis of …
and precise diagnosis of breast cancer plays a pivotal role to improve the prognosis of …
Pan-cancer integrative histology-genomic analysis via multimodal deep learning
The rapidly emerging field of computational pathology has demonstrated promise in
developing objective prognostic models from histology images. However, most prognostic …
developing objective prognostic models from histology images. However, most prognostic …
A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer
Breast cancer is a heterogeneous disease with variable survival outcomes. Pathologists
grade the microscopic appearance of breast tissue using the Nottingham criteria, which are …
grade the microscopic appearance of breast tissue using the Nottingham criteria, which are …
Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer
For prostate cancer patients, the Gleason score is one of the most important prognostic
factors, potentially determining treatment independent of the stage. However, Gleason …
factors, potentially determining treatment independent of the stage. However, Gleason …
Deep learning with multimodal representation for pancancer prognosis prediction
A Cheerla, O Gevaert - Bioinformatics, 2019 - academic.oup.com
Motivation Estimating the future course of patients with cancer lesions is invaluable to
physicians; however, current clinical methods fail to effectively use the vast amount of …
physicians; however, current clinical methods fail to effectively use the vast amount of …
Deep learning in mammography and breast histology, an overview and future trends
A Hamidinekoo, E Denton, A Rampun, K Honnor… - Medical image …, 2018 - Elsevier
Recent improvements in biomedical image analysis using deep learning based neural
networks could be exploited to enhance the performance of Computer Aided Diagnosis …
networks could be exploited to enhance the performance of Computer Aided Diagnosis …
Digital image analysis in breast pathology—from image processing techniques to artificial intelligence
Breast cancer is the most common malignant disease in women worldwide. In recent
decades, earlier diagnosis and better adjuvant therapy have substantially improved patient …
decades, earlier diagnosis and better adjuvant therapy have substantially improved patient …
A comprehensive review for breast histopathology image analysis using classical and deep neural networks
Breast cancer is one of the most common and deadliest cancers among women. Since
histopathological images contain sufficient phenotypic information, they play an …
histopathological images contain sufficient phenotypic information, they play an …
Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies
B Ehteshami Bejnordi, M Mullooly, RM Pfeiffer… - Modern …, 2018 - nature.com
The breast stromal microenvironment is a pivotal factor in breast cancer development,
growth and metastases. Although pathologists often detect morphologic changes in stroma …
growth and metastases. Although pathologists often detect morphologic changes in stroma …