4.8 Article

A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology

Related references

Note: Only part of the references are listed.
Article Computer Science, Artificial Intelligence

A Survey on Vision Transformer

Kai Han et al.

Summary: Transformer, a deep neural network with a self-attention mechanism, has been initially used in natural language processing and is now gaining attention in computer vision tasks. Transformer-based models perform as well as or even better than convolutional and recurrent neural networks in various visual benchmarks. This paper reviews vision transformer models, categorizes them based on different tasks, and analyzes their advantages and disadvantages. The discussed categories include backbone network, high/mid-level vision, low-level vision, and video processing. Efficient methods for applying transformer in real device-based applications are also explored. The challenges and further research directions for vision transformers are discussed as well.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2023)

Article Biochemistry & Molecular Biology

QuPath: The global impact of an open source digital pathology system

M. P. Humphries et al.

Summary: QuPath, created at Queen's University Belfast, is the most widely used image analysis software program globally, addressing various needs in tissue-based image analysis and serving as the system of choice for researchers in scientific research.

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL (2021)

Article Oncology

Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

Hyuna Sung et al.

Summary: The global cancer burden in 2020 saw an estimated 19.3 million new cancer cases and almost 10.0 million cancer deaths. Female breast cancer surpassed lung cancer as the most commonly diagnosed cancer, while lung cancer remained the leading cause of cancer death. These trends are expected to rise in 2040, with transitioning countries experiencing a larger increase compared to transitioned countries due to demographic changes and risk factors associated with globalization and a growing economy. Efforts to improve cancer prevention measures and provision of cancer care in transitioning countries will be crucial for global cancer control.

CA-A CANCER JOURNAL FOR CLINICIANS (2021)

Article Multidisciplinary Sciences

Ensembled deep learning model outperforms human experts in diagnosing biliary atresia from sonographic gallbladder images

Wenying Zhou et al.

Summary: A deep learning model is developed to assist in the diagnosis of biliary atresia (BA) using sonographic gallbladder images, outperforming human experts in multi-center external validation. The model not only improves the performance of human experts, but also maintains expert-level performance in diagnoses based on smartphone photos and video sequences.

NATURE COMMUNICATIONS (2021)

Review Biochemistry & Molecular Biology

Deep learning in histopathology: the path to the clinic

Jeroen van der Laak et al.

Summary: Recent advancements in machine learning have shown great potential to enhance medical diagnostics, particularly in the field of histopathology. However, challenges remain in implementing these techniques in clinical settings.

NATURE MEDICINE (2021)

Article Immunology

Prognostic Value of Tumor-Infiltrating Lymphocytes and Tertiary Lymphoid Structures in Epstein-Barr Virus-Associated and -Negative Gastric Carcinoma

Na Cheng et al.

Summary: The study found that in EBV-negative gastric carcinoma, the grade of TILs was correlated with the presence of TLS, and patients with high TILs grade and TLS presence showed survival benefits. TILs and TLS were independent prognostic factors in EBVnGC.

FRONTIERS IN IMMUNOLOGY (2021)

Article Medical Informatics

Correspondence to: Dr Jakob Nikolas Kather, @jnkath For the Genomic Data Commons data portal see https://portal.gdc.cancer.gov See Online for appendix

Hannah Sophie Muti et al.

Summary: This study aimed to develop and validate deep learning-based classifiers to detect microsatellite instability and EBV status in gastric cancer tissue samples. The results showed that the deep learning-based classifier had high accuracy in detecting microsatellite instability and moderate effectiveness in detecting EBV status.

LANCET DIGITAL HEALTH (2021)

Article Gastroenterology & Hepatology

Clinical-Grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning

Amelie Echle et al.

GASTROENTEROLOGY (2020)

Article Multidisciplinary Sciences

Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains

Nikhil Naik et al.

NATURE COMMUNICATIONS (2020)

Article Biochemistry & Molecular Biology

Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer

Jakob Nikolas Kather et al.

NATURE MEDICINE (2019)

Article Oncology

Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology

Kaustav Bera et al.

NATURE REVIEWS CLINICAL ONCOLOGY (2019)

Article Biochemistry & Molecular Biology

Comprehensive molecular characterization of clinical responses to PD-1 inhibition in metastatic gastric cancer

Seung Tae Kim et al.

NATURE MEDICINE (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 Oncology

Immune Activation and Benefit From Avelumab in EBV-Positive Gastric Cancer

Anshuman Panda et al.

JNCI-Journal of the National Cancer Institute (2017)

Article Oncology

Immune Activation and Benefit From Avelumab in EBV-Positive Gastric Cancer

Anshuman Panda et al.

JNCI-Journal of the National Cancer Institute (2017)

Article Multidisciplinary Sciences

Comprehensive molecular characterization of gastric adenocarcinoma

Adam J. Bass et al.

NATURE (2014)