Machine Learning Methods for Histopathological Image Analysis

By Daisuke Komura, Shumpei Ishikawa. Computational and Structural Biotechnology Journal, 2017

855 citations recorded by the source corpus.

Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/7e355a8f42becb6648efc4cf0c129a7f560789be

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Digital Pathology and Artificial Intelligence (2019, background): “For this reason, lack of color normalization (standardization) in AI pipeline could negatively affect the performance of machine learning algorithms [10].”
Pathology Image Analysis Using Segmentation Deep Learning Algorithms. (2019, background): “Although there have previously been in-depth reviews on using machine learning methods, including deep learning, in digital pathology image analysis, 4,23 this is the ﬁ rst review of the implementations of segmentation deep learning algorithms for WSI analysis.”
Accurate brain tumor detection using deep convolutional neural network (2022, background): “Extracting features with signiﬁcant discriminatory information lead to achieving high accuracy [15].”
WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020, positive): “In histopathology applications—studying tissue slides under a microscope—this variation can arise from sources like differences in the patient population or in slide staining and image acquisition (Veta et al., 2016; Komura and Ishikawa, 2018; Tellez et al., 2019).”
Causability and explainability of artificial intelligence in medicine (2019, background): “…data available), the time needed from the pathologist (expensive labeling), insufficient labels (region of interest), different levels of magnification (resulting in different levels of information), color variation and artifacts (sliced and placed on glass slides) etc. (Komura & Ishikawa, 2018).”
Self supervised contrastive learning for digital histopathology (2020, methodology): “d self-supervised methods that can utilize unlabeled data, and semi-supervised methods that use partially labeled data, have been found to signiﬁcantly improve task performance Peikari et al. (2018); Komura and Ishikawa (2018); Campanella et al. (2019). A common reason for overﬁtting to neural netwo”
Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging (2023, background): “AI also accelerates the move towards digital pathology, converting traditional glass slides into digital scans for remote diagnostics and collaborative work—both of which are vital in the digital age of telemedicine [47].”
Breast Tumor Cellularity Assessment using Deep Neural Networks (2019, methodology): “ng malignant cells and then computing the tumor’s area [29]. Many efforts have been devoted to developing supervised and unsupervised methods for automated cell and nuclear segmentation and detection [44,21]. Supervised segmentation models have superior performance but require hand-labeled nuclear m”
Artificial intelligence and machine learning for medical imaging: A technology review. (2021, neutral): “Classification can be binary, like in determining whether a pathology is present or not in an image [25,27], involve multiple classes, as in determining a particular pathology among several labels [28 – 30], or concern not the whole image but each pixel, as done for image segmentation [31,32].”
Artificial intelligence in oncology (2020, neutral): “41,42”
The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis (2020, background): “However, these public datasets are focused on a specific disease or task and they cannot be employed for other applications [9].”