☆ 4.7 Article

Improved accuracy in colorectal cancer tissue decomposition through refinement of established deep learning solutions

SCIENTIFIC REPORTS (2023)

Related references

Note: Only part of the references are listed.

Article Computer Science, Information Systems

Few-Shot Learning with Collateral Location Coding and Single-Key Global Spatial Attention for Medical Image Classification

Wenjing Shuai et al.

Summary: This study discusses the issues existing in deep learning models for medical image classification and proposes a new algorithm solution to improve the generalization ability and classification accuracy of the model by explicitly utilizing location information and designing global spatial attention.

ELECTRONICS (2022)

Add to Collection

Article Medicine, General & Internal

General Roadmap and Core Steps for the Development of AI Tools in Digital Pathology

Yasmine Makhlouf et al.

Summary: This article emphasizes the importance of integrating artificial intelligence tools into the tissue diagnostic workflow and proposes a general pathway and core steps to build an AI digital pathology tool. The collaboration between AI scientists and pathologists is highlighted as crucial for the success of the final product.

DIAGNOSTICS (2022)

Add to Collection

Article Chemistry, Multidisciplinary

H&E Multi-Laboratory Staining Variance Exploration with Machine Learning

Fabi Prezja et al.

Summary: In this study, the staining variance between different laboratories on three tissue types was evaluated using machine learning. The results showed that both laboratory effects and tissue type bias contributed to the inter-laboratory staining variance.

APPLIED SCIENCES-BASEL (2022)

Add to Collection

Article Computer Science, Hardware & Architecture

Combining neural and symbolic approaches to solve the Picasso problem: A first step

Valentina Gliozzi et al.

Summary: This work establishes a connection between Convolutional Neural Networks and Answer Set Programming to address the Picasso Problem in image detection. By utilizing the neural network approach for image recognition and logical rules for identifying well-formed images, preliminary experiments show promising results in solving the Picasso Problem.

DISPLAYS (2022)

Add to Collection

Article Multidisciplinary Sciences

DeepFake knee osteoarthritis X-rays from generative adversarial neural networks deceive medical experts and offer augmentation potential to automatic classification

Fabi Prezja et al.

Summary: Recent developments in deep learning have impacted medical science, but privacy issues and regulatory frameworks have hindered data sharing. Generating medically accurate synthetic data can address privacy concerns and enhance deep learning.

SCIENTIFIC REPORTS (2022)

Add to Collection

Proceedings Paper Computer Science, Artificial Intelligence

RandStainNA: Learning Stain-Agnostic Features from Histology Slides by Bridging Stain Augmentation and Normalization

Yiqing Shen et al.

Summary: Stain variations often limit the generalization ability of deep learning approaches in digital histopathology analysis. In this study, the researchers propose a new method called RandStainNA, which unifies stain normalization and stain augmentation to train a stain agnostic deep learning model. The results show that RandStainNA improves the model's ability to handle different stain styles.

MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT II (2022)

Add to Collection

Article Gastroenterology & Hepatology

Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning

Korsuk Sirinukunwattana et al.

Summary: This study utilized deep learning to analyze histological images of colorectal cancer tissue samples and successfully predicted consensus molecular subtypes, addressing the issue of molecular classification. The image-based classification method accurately identified samples that were unclassifiable by RNA expression analysis, showing potential for biological stratification.

GUT (2021)

Add to Collection

Article Medicine, General & Internal

Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence

K. S. Wang et al.

Summary: This study proposed a novel AI-based method for pathological image analysis to assist in the diagnosis of colorectal cancer (CRC). Through extensive training and validation, the method showed excellent performance in multi-center CRC diagnosis, outperforming most experienced expert pathologists.

BMC MEDICINE (2021)

Add to Collection

Article Automation & Control Systems

Colorectal Histology Tumor Detection Using Ensemble Deep Neural Network

Sourodip Ghosh et al.

Summary: In this study, an Ensemble Deep Neural Network was proposed to detect tumors in colorectal histology images, achieving high accuracies on two separate datasets and a benchmark accuracy when the datasets were combined. The utilization of resourced data led to results that outperformed existing works.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2021)

Add to Collection

Article Computer Science, Information Systems

Deep Learning Techniques for the Classification of Colorectal Cancer Tissue

Min-Jen Tsai et al.

Summary: This study proposed a classification methodology for colorectal cancer histological images based on deep learning technology, with significantly improved recognition accuracy compared to existing methods after training and parameter optimization.

ELECTRONICS (2021)

Add to Collection

Article Medicine, General & Internal

Deep learning for prediction of colorectal cancer outcome: a discovery and validation study

Ole-Johan Skrede et al.

LANCET (2020)

Add to Collection

Review Biology

A comprehensive review of deep learning in colon cancer

Ishak Pacal et al.

COMPUTERS IN BIOLOGY AND MEDICINE (2020)

Add to Collection

Article Medicine, General & Internal

Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study

Jakob Nikolas Kather et al.

PLOS MEDICINE (2019)

Add to Collection

Article Biochemistry & Molecular Biology

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

Jakob Nikolas Kather et al.

NATURE MEDICINE (2019)

Add to Collection

Article Computer Science, Theory & Methods

A survey on Image Data Augmentation for Deep Learning

Connor Shorten et al.

JOURNAL OF BIG DATA (2019)

Add to Collection

Proceedings Paper Computer Science, Artificial Intelligence

Multi-task Learning of a Deep K-Nearest Neighbour Network for Histopathological Image Classification and Retrieval

Tingying Peng et al.

MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2019, PT I (2019)

Add to Collection

Proceedings Paper Imaging Science & Photographic Technology

CROSS-MODALITY AUGMENTATION OF BRAIN MR IMAGES USING A NOVEL PAIRWISE GENERATIVE ADVERSARIAL NETWORK FOR ENHANCED GLIOMA CLASSIFICATION

Chenjie Ge et al.

2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) (2019)

Add to Collection

Article Multidisciplinary Sciences

Deep learning based tissue analysis predicts outcome in colorectal cancer

Dmitrii Bychkov et al.

SCIENTIFIC REPORTS (2018)

Add to Collection

Article Oncology

Prognostic markers for colorectal cancer: estimating ploidy and stroma

H. E. Danielsen et al.

ANNALS OF ONCOLOGY (2018)

Add to Collection

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

Add to Collection

Article Genetics & Heredity

Stromal contribution to the colorectal cancer transcriptome

Claudio Isella et al.

NATURE GENETICS (2015)

Add to Collection

Article Radiology, Nuclear Medicine & Medical Imaging

Promise and pitfalls of quantitative imaging in oncology clinical trials

Brenda F. Kurland et al.

MAGNETIC RESONANCE IMAGING (2012)

Add to Collection

Review Oncology

Clinical Applications of Metabolomics in Oncology: A Review

Jennifer L. Spratlin et al.

CLINICAL CANCER RESEARCH (2009)

Add to Collection

Review Oncology

Quantitative imaging biomarkers in neuro-oncology

Adam D. Waldman et al.

NATURE REVIEWS CLINICAL ONCOLOGY (2009)

Add to Collection

Review Oncology

Quantitative imaging biomarkers in the clinical development of targeted therapeutics: current and future perspectives

James P. B. O'Connor et al.

LANCET ONCOLOGY (2008)

Add to Collection

Article Computer Science, Artificial Intelligence

Extremely randomized trees

P Geurts et al.

MACHINE LEARNING (2006)

Add to Collection

© Peeref 2019-2024. All rights reserved.