Machine Learning Methods for Histopathological Image Analysis: A Review

Matos, Jonathan de; Ataky, Steve Tsham Mpinda; Britto, Alceu de Souza; Oliveira, Luiz S.; Koerich, Alessandro L.

doi:10.3390/electronics10050562

Cited by 44 publications

(13 citation statements)

References 204 publications

(318 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several researchers and clinicians have since looked into the BreaKHis dataset to create automated and accurate methods for distinguishing between various breast histopathological images utilizing CNNs as the key building blocks (Das et al, 2020). A comprehensive analysis of current literary studied the BreaKHis database for MD and MI prostate cancer categorizations ( de Matos et al, 2021). A histopathological pixel intensities patch has been employed to create a training neural network derivative of the AlexNet (Arjmand et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

Breast microscopic cancer segmentation and classification using unique 4‐qubit‐quantum model

Amin

Sharif

Fernandes

et al. 2022

Microscopy Res & Technique

View full text Add to dashboard Cite

The visual inspection of histopathological samples is the benchmark for detecting breast cancer, but a strenuous and complicated process takes a long time of the pathologist practice. Deep learning models have shown excellent outcomes in clinical diagnosis and image processing and advances in various fields, including drug development, frequency simulation, and optimization techniques. However, the resemblance of histopathologic images of breast cancer and the inclusion of stable and infected tissues in different areas make detecting and classifying tumors on entire slide images more difficult. In breast cancer, a correct diagnosis is needed for complete care in a limited amount of time. An effective detection can relieve the pathologist's workload and mitigate diagnostic subjectivity. Therefore, this research work investigates improved the pre‐trained xception and deeplabv3+ design semantic model. The model has been trained on input images with ground masks on the tuned parameters that significantly improve the segmentation of ultrasound breast images into respective classes, that is, benign/malignant. The segmentation model delivered an accuracy of greater than 99% to prove the model's effectiveness. The segmented images and histopathological breast images are transferred to the 4‐qubit‐quantum circuit with six‐layered architecture to detect breast malignancy. The proposed framework achieved remarkable performance as contrasted to currently published methodologies. Highlights This research proposed hybrid semantic model using pre‐trained xception and deeplabv3 for breast microscopic cancer classification in to benign and malignant classes at accuracy of 95% accuracy, 99% accuracy for detection of breast malignancy.

show abstract

Section: Related Workmentioning

confidence: 99%

Breast microscopic cancer segmentation and classification using unique 4‐qubit‐quantum model

Amin

Sharif

Fernandes

et al. 2022

Microscopy Res & Technique

View full text Add to dashboard Cite

show abstract

“…AI stands to disrupt this process, and promises to result in faster, more accurate diagnoses, with more uniform standardization [ 152 ]. ML for histological analysis has made significant progress over the last decade [ 153 ]. ML programs analyse digitised histopathological slides, and are able to detect both macro and micro patterns, including region texture, shape, and cellular morphology, and process these features to make accurate histopathological conclusions [ 153 , 154 ].…”

Section: Post-operative Phasementioning

confidence: 99%

Artificial Intelligence in Brain Tumour Surgery—An Emerging Paradigm

Williams

Horsfall

Funnell

et al. 2021

Cancers

View full text Add to dashboard Cite

Artificial intelligence (AI) platforms have the potential to cause a paradigm shift in brain tumour surgery. Brain tumour surgery augmented with AI can result in safer and more effective treatment. In this review article, we explore the current and future role of AI in patients undergoing brain tumour surgery, including aiding diagnosis, optimising the surgical plan, providing support during the operation, and better predicting the prognosis. Finally, we discuss barriers to the successful clinical implementation, the ethical concerns, and we provide our perspective on how the field could be advanced.

show abstract

“…Further, histology images are very large, which makes their analysis cumbersome and timeconsuming. With advances in Computer-Aided-Diagnosis (CAD), AI techniques, especially Machine Learning (ML) and DL, have the potential to address the aforementioned bottlenecks [1][2][3][4] . These techniques can identify discriminative morphological patterns from large datasets to diagnose histology images in a standardized and objective manner.…”

Section: Background and Summarymentioning

confidence: 99%

BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images

Anniciello¹,

Pati²,

Riccio³

et al. 2021

Preprint

View full text Add to dashboard Cite

Breast cancer is the most commonly diagnosed cancer and registers the highest number of deaths for women with cancer. Recent advancements in diagnostic activities combined with large-scale screening policies have significantly lowered the mortality rates for breast cancer patients. However, the manual inspection of tissue slides by pathologists is cumbersome, time-consuming, and is subject to significant inter-and intra-observer variability. Recently, the advent of whole-slide scanning systems have empowered the rapid digitization of pathology slides, and enabled to develop digital workflows. These progress further enable to leverage Artificial Intelligence (AI) to assist, automate, and augment pathological diagnosis. But AI techniques, especially Deep Learning (DL), require a large amount of high-quality annotated data to learn from. Constructing such task-specific datasets poses several challenges, such as, data-acquisition level constrains, time-consuming and expensive annotations, and anonymization of patient information, etc. In this paper, we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of annotated Hematoxylin & Eosin (H&E)-stained images to advance the characterization of breast lesions. BRACS contains 547 Whole-Slide Images (WSIs), and 4539 Regions of Interest (RoIs) extracted from the WSIs. Each WSI, and respective RoIs, are annotated by the consensus of three board-certified pathologists into different lesion categories. Specifically, BRACS includes three lesion types, i.e., benign, malignant and atypical, which are further subtyped into seven categories. The included RoIs exhibit large variability in dimensions, and incorporate the usual tissue-preparation and staining artifacts to bestow a realistic breast cancer diagnosis. It is, to the best of our knowledge, the largest annotated dataset for breast cancer subtyping both at WSI-and RoI-level. Further, by including the understudied atypical lesions, BRACS offers an unique opportunity for leveraging AI to better understand their characteristics. We encourage AI practitioners to develop and evaluate novel algorithms on the BRACS dataset to further breast cancer diagnosis and patient care.

show abstract

Machine Learning Methods for Histopathological Image Analysis: A Review

Cited by 44 publications

References 204 publications

Breast microscopic cancer segmentation and classification using unique 4‐qubit‐quantum model

Breast microscopic cancer segmentation and classification using unique 4‐qubit‐quantum model

Artificial Intelligence in Brain Tumour Surgery—An Emerging Paradigm

BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images

Contact Info

Product

Resources

About