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 single‐cell optical image studies
Optical imaging technology that has the advantages of high sensitivity and cost‐
effectiveness greatly promotes the progress of nondestructive single‐cell studies. Complex …
effectiveness greatly promotes the progress of nondestructive single‐cell studies. Complex …
Hyperspectral imaging for the detection of glioblastoma tumor cells in H&E slides using convolutional neural networks
Hyperspectral imaging (HSI) technology has demonstrated potential to provide useful
information about the chemical composition of tissue and its morphological features in a …
information about the chemical composition of tissue and its morphological features in a …
[HTML][HTML] Artificial intelligence and digital pathology: Opportunities and implications for immuno-oncology
The field of immuno-oncology has expanded rapidly over the past decade, but key questions
remain. How does tumour-immune interaction regulate disease progression? How can we …
remain. How does tumour-immune interaction regulate disease progression? How can we …
[HTML][HTML] Mining textural knowledge in biological images: Applications, methods and trends
Texture analysis is a major task in many areas of computer vision and pattern recognition,
including biological imaging. Indeed, visual textures can be exploited to distinguish specific …
including biological imaging. Indeed, visual textures can be exploited to distinguish specific …
[HTML][HTML] Multi-path residual attention network for cancer diagnosis robust to a small number of training data of microscopic hyperspectral pathological images
Duct cancer is a malignant disease with higher mortality rates in males than in females,
emphasizing the need for early diagnosis to improve treatment outcomes. Although various …
emphasizing the need for early diagnosis to improve treatment outcomes. Although various …
Deep learning models for digital pathology
Histopathology images; microscopy images of stained tissue biopsies contain fundamental
prognostic information that forms the foundation of pathological analysis and diagnostic …
prognostic information that forms the foundation of pathological analysis and diagnostic …
Transfer Learning and Data Augmentation for Semantic Segmentation in Histopathology
G Cantisani - 2018 - webthesis.biblio.polito.it
The segmentation of Region Of Interests (ROIs) in high-resolution histopathological images
is a task with high clinical relevance which requires large amounts of reading time from …
is a task with high clinical relevance which requires large amounts of reading time from …
Analyzing cancers in digitized histopathology images
A Ben Taieb - 2018 - summit.sfu.ca
Cancer refers to a group of diseases characterized by an uncontrolled proliferation of cells
with underlying genetic mutations that can be arranged in solid masses forming tumors. The …
with underlying genetic mutations that can be arranged in solid masses forming tumors. The …