Deep neural network models for computational histopathology: A survey
CL Srinidhi, O Ciga, AL Martel - Medical image analysis, 2021 - Elsevier
Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …
underlying mechanisms contributing to disease progression and patient survival outcomes …
[HTML][HTML] The impact of pre-and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis
Recently, deep learning frameworks have rapidly become the main methodology for
analyzing medical images. Due to their powerful learning ability and advantages in dealing …
analyzing medical images. Due to their powerful learning ability and advantages in dealing …
Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology
Stain variation is a phenomenon observed when distinct pathology laboratories stain tissue
slides that exhibit similar but not identical color appearance. Due to this color shift between …
slides that exhibit similar but not identical color appearance. Due to this color shift between …
A comprehensive review of deep learning in colon cancer
Deep learning has emerged as a leading machine learning tool in object detection and has
attracted attention with its achievements in progressing medical image analysis …
attracted attention with its achievements in progressing medical image analysis …
[HTML][HTML] Machine learning methods for histopathological image analysis
D Komura, S Ishikawa - Computational and structural biotechnology journal, 2018 - Elsevier
Abundant accumulation of digital histopathological images has led to the increased demand
for their analysis, such as computer-aided diagnosis using machine learning techniques …
for their analysis, such as computer-aided diagnosis using machine learning techniques …
Deep learning in microscopy image analysis: A survey
Computerized microscopy image analysis plays an important role in computer aided
diagnosis and prognosis. Machine learning techniques have powered many aspects of …
diagnosis and prognosis. Machine learning techniques have powered many aspects of …
Staingan: Stain style transfer for digital histological images
Digitized Histological diagnosis is in increasing demand. However, color variations due to
various factors are imposing obstacles to the diagnosis process. The problem of stain color …
various factors are imposing obstacles to the diagnosis process. The problem of stain color …
[HTML][HTML] An investigation of XGBoost-based algorithm for breast cancer classification
Breast cancer is one of the leading cancers affecting women around the world. The
Computer-Aided Diagnosis (CAD) system is a powerful tool to assist pathologists during the …
Computer-Aided Diagnosis (CAD) system is a powerful tool to assist pathologists during the …
Neural image compression for gigapixel histopathology image analysis
We propose Neural Image Compression (NIC), a two-step method to build convolutional
neural networks for gigapixel image analysis solely using weak image-level labels. First …
neural networks for gigapixel image analysis solely using weak image-level labels. First …
Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline
Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated
morphologies. Standard semi-quantitative scoring approaches, however, are coarse …
morphologies. Standard semi-quantitative scoring approaches, however, are coarse …