4.6 Article

Efficient and Highly Accurate Diagnosis of Malignant Hematological Diseases Based on Whole-Slide Images Using Deep Learning

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Artificial Intelligence

Deep learning for bone marrow cell detection and classification on whole-slide images

Ching-Wei Wang et al.

Summary: This study developed an efficient and fully automatic hierarchical deep learning framework for bone marrow nucleated differential count (NDC) whole-slide image (WSI) analysis. The framework includes rapid localization, cell identification, and result integration steps, achieving high recall and accuracy in cell counting with superior performance compared to existing benchmark methods.

MEDICAL IMAGE ANALYSIS (2022)

Article Engineering, Biomedical

Automated Detection of B Cell and T Cell Acute Lymphoblastic Leukaemia Using Deep Learning

K. K. Anilkumar et al.

Summary: This study utilized deep learning techniques for classifying Acute Lymphoblastic Leukaemia (ALL) and achieved a high classification accuracy by processing and analyzing images of leukemic cells. The study did not use image segmentation and feature extraction techniques, making it a valuable research in the field of ALL classification.
Article Oncology

Deep learning detects acute myeloid leukemia and predicts NPM1 mutation status from bone marrow smears

Jan-Niklas Eckardt et al.

Summary: In this study, a multi-step deep learning approach was applied to automatically segment cells from bone marrow images, distinguish between AML samples and healthy controls with high accuracy, and predict the mutation status of NPM1. Unreported morphologic cell features were identified using occlusion sensitivity maps, enabling the DL model to provide accurate class predictions.

LEUKEMIA (2022)

Article Hematology

Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set

Christian Matek et al.

Summary: This study successfully used convolutional neural networks to classify bone marrow cell cytomorphology, achieving high precision and recall. It represents a step forward in automated evaluation of bone marrow cell morphology using state-of-the-art image-classification algorithms, serving as a reference for future AI-based approaches.
Article Chemistry, Analytical

IoT Application of Transfer Learning in Hybrid Artificial Intelligence Systems for Acute Lymphoblastic Leukemia Classification

Krzysztof Palczynski et al.

Summary: This study developed a method for accurately classifying acute lymphoblastic leukemia using a hybrid artificial intelligence system based on a neural network architecture, with an accuracy rate of up to 97.4%. The approach proved effective and promising for diagnosing other blood diseases as well.

SENSORS (2021)

Article Multidisciplinary Sciences

An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning

Chi-Long Chen et al.

Summary: Deep learning for digital pathology is hindered by the high spatial resolution of WSIs, requiring patch-based methods and manual contouring. This study introduces a whole-slide training method for lung cancer classification based on slide-level diagnoses using deep learning.

NATURE COMMUNICATIONS (2021)

Article Engineering, Biomedical

Data-efficient and weakly supervised computational pathology on whole-slide images

Ming Y. Lu et al.

Summary: The CLAM method utilizes attention-based learning to identify subregions with high diagnostic value for accurate classification of whole-slide images. It can localize well-known morphological features without the need for spatial labels, outperforming standard weakly supervised classification algorithms, and adapt to independent test cohorts, smartphone microscopy, and varying tissue content.

NATURE BIOMEDICAL ENGINEERING (2021)

Article Multidisciplinary Sciences

AI-based pathology predicts origins for cancers of unknown primary

Ming Y. Lu et al.

Summary: Cancer of unknown primary (CUP) is a difficult diagnosis as the primary site of tumor origin cannot be determined. The deep-learning-based algorithm TOAD provides a differential diagnosis for the origin of the primary tumor, achieving high accuracy on test sets and reducing the occurrence of CUP by assisting in assigning differential diagnoses for complicated cases.

NATURE (2021)

Article Biochemistry & Molecular Biology

Triage-driven diagnosis of Barrett's esophagus for early detection of esophageal adenocarcinoma using deep learning

Marcel Gehrung et al.

Summary: Deep learning methods have been successfully integrated with expert knowledge and clinical decision pathways to automate the analysis of Cytosponge-TFF3 test samples for the early detection of Barrett's esophagus. By defining triage classes based on pathologists' decision patterns, the workload of pathologists can be reduced by 57% without compromising diagnostic performance. The clinician-in-the-loop deep learning system streamlines the workflow for Barrett's esophagus detection while maintaining the accuracy of manual review.

NATURE MEDICINE (2021)

Article Computer Science, Artificial Intelligence

GCTI-SN: Geometry-inspired chemical and tissue invariant stain normalization of microscopic medical images

Anubha Gupta et al.

MEDICAL IMAGE ANALYSIS (2020)

Article Automation & Control Systems

Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis

Xi Wang et al.

IEEE TRANSACTIONS ON CYBERNETICS (2020)

Article Computer Science, Artificial Intelligence

Predicting tumour mutational burden from histopathological images using multiscale deep learning

Mika S. Jain et al.

NATURE MACHINE INTELLIGENCE (2020)

Article Biochemistry & Molecular Biology

Clinical-grade computational pathology using weakly supervised deep learning on whole slide images

Gabriele Campanella et al.

NATURE MEDICINE (2019)

Article Medicine, General & Internal

Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network

Nizar Ahmed et al.

DIAGNOSTICS (2019)

Article Biochemistry & Molecular Biology

Deep learning-based classification of mesothelioma improves prediction of patient outcome

Pierre Courtiol et al.

NATURE MEDICINE (2019)

Article Medical Laboratory Technology

Artificial Intelligence-Based Breast Cancer Nodal Metastasis Detection

Yun Liu et al.

ARCHIVES OF PATHOLOGY & LABORATORY MEDICINE (2019)

Article Health Care Sciences & Services

Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer

Kunal Nagpal et al.

NPJ DIGITAL MEDICINE (2019)

Article Pathology

Is a 500-Cell Count Necessary for Bone Marrow Differentials? A Proposed Analytical Method for Validating a Lower Cutoff

Ahmed A. Abdulrahman et al.

AMERICAN JOURNAL OF CLINICAL PATHOLOGY (2018)

Article Biochemistry & Molecular Biology

Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning

Nicolas Coudray et al.

NATURE MEDICINE (2018)

Article Anatomy & Morphology

Classification of acute lymphoblastic leukemia using deep learning

Amjad Rehman et al.

MICROSCOPY RESEARCH AND TECHNIQUE (2018)

Article Medicine, General & Internal

Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer

Babak Ehteshami Bejnordi et al.

JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2017)

Article Gastroenterology & Hepatology

Hematologic and Oncologic Diseases and the Liver

Marvin M. Singh et al.

CLINICS IN LIVER DISEASE (2011)

Review Hematology

ICSH guidelines for the standardization of bone marrow specimens and reports

S. -H. Lee et al.

INTERNATIONAL JOURNAL OF LABORATORY HEMATOLOGY (2008)

Article Computer Science, Artificial Intelligence

On the learnability and design of output codes for multiclass problems

K Crammer et al.

MACHINE LEARNING (2002)