The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis

Computers in Biology and Medicine | 21/11/2020

Recently, deep learning frameworks have rapidly become the main methodology for analyzing medical images. Due to their powerful learning ability and advantages in dealing with complex patterns, deep learning algorithms are ideal for image analysis challenges, particularly in the field of digital pathology. 

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Multi-tissue and multi-scale approach for nuclei segmentation in H&E stained images

BioMedical Engineering OnLine | 20/06/2018

Accurate nuclei detection and segmentation in histological images is essential for many clinical purposes. While manual annotations are time-consuming and operator-dependent, full automated segmentation remains a challenging task due to the high variability of cells intensity, size and morphology. Most of the proposed algorithms for the automated segmentation of nuclei were designed for specific organ or tissues.

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Stain Color Adaptive Normalization (SCAN) algorithm: Separation and standardization of histological stains in digital pathology

Computer Methods and Programs in Biomedicine | 17/04/2020

The diagnosis of histopathological images is based on the visual analysis of tissue slices under a light microscope. However, the histological tissue appearance may assume different color intensities depending on the staining process, operator ability and scanner specifications. This stain variability affects the diagnosis of the pathologist and decreases the accuracy of computer-aided diagnosis systems.

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Impact of stain normalization and patch selection on the performance of convolutional neural networks in histological breast and prostate cancer classification

Computer Methods and Programs in Biomedicine Update | 13/02/2021

Recently, deep learning has rapidly become the methodology of choice in digital pathology image analysis. However, due to the current challenges of digital pathology (color stain variability, large images, etc.), specific pre-processing steps are required to train a reliable deep learning model.

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A hybrid deep learning approach for gland segmentation in prostate histopathological images

Artificial Intelligence in Medicine | 16/04/2021

In digital pathology, the morphology and architecture of prostate glands have been routinely adopted by pathologists to evaluate the presence of cancer tissue. The manual annotations are operator-dependent, error-prone and time-consuming. The automated segmentation of prostate glands can be very challenging too due to large appearance variation and serious degeneration of these histological structures.

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Automatic discrimination of neoplastic epithelium and stromal response in breast carcinoma

Computers in Biology and Medicine | 11/05/2019

In breast carcinoma, epithelial–stromal interactions play a pivotal role in tumor formation and progression, and it must be accurately assessed for a correct extraction of predictive and prognostic biomarkers. Evaluation of preoperative (baseline) neoplasia/stroma ratio and the enumeration of tumor infiltrating lymphocytes (TIL) represent only two conditions in which precise discrimination of cancer epithelium and stromal reaction are relevant.

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Fully automated quantitative assessment of hepatic steatosis in liver transplants

Computers in Biology and Medicine | 29/05/2020

The presence of macro- and microvesicular steatosis is one of the major risk factors for liver transplantation. An accurate assessment of the steatosis percentage is crucial for determining liver graft transplantability, which is currently based on the pathologists’ visual evaluations on liver histology specimens.

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Karpinski Score under Digital Investigation: A Fully Automated Segmentation Algorithm to Identify Vascular and Stromal Injury of Donors’ Kidneys

Electronics | 08/10/2020

In kidney transplantations, the evaluation of the vascular structures and stromal areas is crucial for determining kidney acceptance, which is currently based on the pathologist’s visual evaluation. In this context, an accurate assessment of the vascular and stromal injury is fundamental to assessing the nephron status.

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