ArticleMedical image analysis2021
Dual attention multiple instance learning with unsupervised complementary loss for COVID-19 screening.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review.Computer methods and programs in biomedicine · 2022Pooled it
- FAD-MIL: a weakly supervised fracture detection model based on X-ray images.Scientific reports · 2026Article
- Preoperative CT deep learning of tumor and visceral adipose tissue combined with postoperative pathology predicts colorectal cancer recurrence: a multicenter study.Frontiers in molecular biosciences · 2026Article
- Reimagining cancer tissue classification: a multi-scale framework based on multi-instance learning for whole slide image classification.Medical & biological engineering & computing · 2025Article
- High performance with fewer labels using semi-weakly supervised learning for pulmonary embolism diagnosis.NPJ digital medicine · 2025Article
- Deep Learning Model for Predicting Lung Adenocarcinoma Recurrence from Whole Slide Images.Cancers · 2024Article
- The TOX-RAGE axis mediates inflammatory activation and lung injury in severe pulmonary infectious diseases.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Deep Learning Methods for Interpretation of Pulmonary CT and X-ray Images in Patients with COVID-19-Related Lung Involvement: A Systematic Review.Journal of clinical medicine · 2023Review
- Bilateral adaptive graph convolutional network on CT based Covid-19 diagnosis with uncertainty-aware consensus-assisted multiple instance learning.Medical image analysis · 2023Article
- uRP: An integrated research platform for one-stop analysis of medical images.Frontiers in radiology · 2023Article
- Modality-based attention and dual-stream multiple instance convolutional neural network for predicting microvascular invasion of hepatocellular carcinoma.Frontiers in oncology · 2023Article
- Developing a weakly supervised deep learning framework for breast cancer diagnosis with HR status based on mammography images.Computational and structural biotechnology journal · 2023Article
- Deep Learning for Detecting COVID-19 Using Medical Images.Bioengineering (Basel, Switzerland) · 2022Article
- Classification and visual explanation for COVID-19 pneumonia from CT images using triple learning.Scientific reports · 2022Article
- DCML: Deep contrastive mutual learning for COVID-19 recognition.Biomedical signal processing and control · 2022Article
- Chest L-Transformer: Local Features With Position Attention for Weakly Supervised Chest Radiograph Segmentation and Classification.Frontiers in medicine · 2022Article
- Densely connected attention network for diagnosing COVID-19 based on chest CT.Computers in biology and medicine · 2021Article
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Authors and funding
6 authors.
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Abstract
Chest computed tomography (CT) based analysis and diagnosis of the Coronavirus Disease 2019 (COVID-19) plays a key role in combating the outbreak of the pandemic that has rapidly spread worldwide. To date, the disease has infected more than 18 million people with over 690k deaths reported. Reverse transcription polymerase chain reaction (RT-PCR) is the current gold standard for clinical diagnosis but may produce false positives; thus, chest CT based diagnosis is considered more viable. However, accurate screening is challenging due to the difficulty in annotation of infected areas, curation of large datasets, and the slight discrepancies between COVID-19 and other viral pneumonia. In this study, we propose an attention-based end-to-end weakly supervised framework for the rapid diagnosis of COVID-19 and bacterial pneumonia based on multiple instance learning (MIL). We further incorporate unsupervised contrastive learning for improved accuracy with attention applied both in spatial and latent contexts, herein we propose Dual Attention Contrastive based MIL (DA-CMIL). DA-CMIL takes as input several patient CT slices (considered as bag of instances) and outputs a single label. Attention based pooling is applied to implicitly select key slices in the latent space, whereas spatial attention learns slice spatial context for interpretable diagnosis. A contrastive loss is applied at the instance level to encode similarity of features from the same patient against representative pooled patient features. Empirical results show that our algorithm achieves an overall accuracy of 98.6% and an AUC of 98.4%. Moreover, ablation studies show the benefit of contrastive learning with MIL.
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