☆ 4.7 Article

Masked autoencoders with handcrafted feature predictions: Transformer for weakly supervised esophageal cancer classification

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2024)

Related references

Note: Only part of the references are listed.

Article Computer Science, Interdisciplinary Applications

StoHisNet: A hybrid multi-classification model with CNN and Transformer for gastric pathology images

Bangkang Fu et al.

Summary: This study proposed a deep learning-based multi-classification method for gastric cancer pathological images. The experimental results showed that the proposed StoHisNet model exhibited good performance and generalization ability in the multi-classification task of gastric histopathological images.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2022)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Deep learning with whole slide images can improve the prognostic risk stratification with III colorectal cancer

Caixia Sun et al.

Summary: This study developed a prognostic signature using deep learning on whole slide images to predict disease-free survival and overall survival in colorectal cancer patients, improving risk stratification and potentially identifying those who may benefit from extended chemotherapy.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2022)

Add to Collection

Article Computer Science, Interdisciplinary Applications

DSNet: A Dual-Stream Framework for Weakly-Supervised Gigapixel Pathology Image Analysis

Tiange Xiang et al.

Summary: We propose a novel weakly-supervised framework for classifying whole slide images (WSIs), which integrates information at both local and regional levels for better classification performance.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2022)

Add to Collection

Article Computer Science, Artificial Intelligence

DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer

Yoni Schirris et al.

Summary: This study proposes a deep learning-based weak label learning method for analyzing tumor tissue whole slide images without pixel-level or tile-level annotations. They utilize self-supervised pre-training and heterogeneity-aware deep multiple instance learning. The method is applied to the prediction of homologous recombination deficiency and microsatellite instability, achieving improved performance.

MEDICAL IMAGE ANALYSIS (2022)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Constrained multiple instance learning for ulcerative colitis prediction using histological images

Rocio del Amor et al.

Summary: This study developed a novel approach based on multiple instance learning framework to detect ulcerative colitis using whole-slide images. The method introduced constraints and weighted embedding to improve accuracy of identification.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2022)

Add to Collection

Article Computer Science, Interdisciplinary Applications

IDA-MIL: Classification of Glomerular with Spike-like Projections via Multiple Instance Learning with Instance-level Data Augmentation

Xi Wu et al.

Summary: This study proposes a classification framework that combines weakly-supervised learning and data augmentation methods for classifying glomeruli with spike-like projections in stage II membranous nephropathy. The framework first trains a MIL model on a dataset with image-level labels and extracts instances containing spike-like projections in the entire glomerular image. Then, an instance-level generative adversarial network (InsGAN) based on the StyleGAN2-ADA model is trained on the spike-like instances obtained by the MIL model to synthesize new spike-like projection instances. The synthesized instances are added to the training dataset to improve the classification performance of MIL.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2022)

Add to Collection

Article Computer Science, Interdisciplinary Applications

IEViT: An enhanced vision transformer architecture for chest X-ray image classification

Gabriel Iluebe Okolo et al.

Summary: This study examines the application of a novel enhanced Vision Transformer model (IEViT) for the task of chest X-ray image classification. The proposed model outperforms the state-of-the-art Vision Transformer (ViT) model on multiple datasets, demonstrating its superior performance and generalization ability for assisting diagnosis using chest X-ray images.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2022)

Add to Collection

Article Computer Science, Artificial Intelligence

Neural Image Compression for Gigapixel Histopathology Image Analysis

David Tellez et al.

Summary: The proposed Neural Image Compression (NIC) method utilizes weak image-level labels to build convolutional neural networks in two steps: compressing image representations and training CNN on these compressed representations. Through contrasting training and evaluation, it is found that NIC successfully integrates visual cues associated with image-level labels.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2021)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Self-Path: Self-Supervision for Classification of Pathology Images With Limited Annotations

Navid Alemi Koohbanani et al.

Summary: The article proposes the Self-Path framework for utilizing unlabeled data in pathology images for semi-supervised learning and domain adaptation, achieving state-of-the-art performance with limited labeled data available.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2021)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Weakly Supervised Deep Ordinal Cox Model for Survival Prediction From Whole-Slide Pathological Images

Wei Shao et al.

Summary: WSI is considered as the gold standard for cancer diagnosis and prognosis. However, existing prediction models do not efficiently utilize ordinal information among patients, and the large size and single label of WSIs complicate the training process.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2021)

Add to Collection

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)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Detection of Prostate Cancer in Whole-Slide Images Through End-to-End Training With Image-Level Labels

Hans Pinckaers et al.

Summary: Prostate cancer is the most prevalent cancer among men in Western countries, and pathologists' evaluation is the gold standard for diagnosis. State-of-the-art convolutional neural networks are often patch-based and require detailed pixel-level annotations for effective training.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks

Jiawen Yao et al.

MEDICAL IMAGE ANALYSIS (2020)

Add to Collection

Article Biochemistry & Molecular Biology

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

Gabriele Campanella et al.

NATURE MEDICINE (2019)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Fast ScanNet: Fast and Dense Analysis of Multi-Gigapixel Whole-Slide Images for Cancer Metastasis Detection

Huangjing Lin et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2019)

Add to Collection

Article Biochemical Research Methods

U-Net: deep learning for cell counting, detection, and morphometry

Thorsten Falk et al.

NATURE METHODS (2019)

Add to Collection

Article Computer Science, Artificial Intelligence

RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification

Shujun Wang et al.

MEDICAL IMAGE ANALYSIS (2019)

Add to Collection

Article Oncology

Worldwide incidence, mortality and time trends for cancer of the oesophagus

Bhawna Gupta et al.

EUROPEAN JOURNAL OF CANCER PREVENTION (2017)

Add to Collection

Article Computer Science, Artificial Intelligence

Dynamic Programming for Instance Annotation in Multi-Instance Multi-Label Learning

Anh T. Pham et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2017)

Add to Collection

Article Computer Science, Artificial Intelligence

A survey on deep learning in medical image analysis

Geert Litjens et al.

MEDICAL IMAGE ANALYSIS (2017)

Add to Collection

Article Biochemical Research Methods

Metastasis detection from whole slide images using local features and random forests

Mira Valkonen et al.

CYTOMETRY PART A (2017)

Add to Collection

Article Engineering, Biomedical

Computer-aided diagnosis from weak supervision: A benchmarking study

Melih Kandemir et al.

COMPUTERIZED MEDICAL IMAGING AND GRAPHICS (2015)

Add to Collection

© Peeref 2019-2024. All rights reserved.