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Found 13 papers, 4 papers with code
Concept-Attention Whitening for Interpretable Skin Lesion Diagnosis
In the former branch, we train the CNN with a CAW layer inserted to perform skin lesion diagnosis.
QMix: Quality-aware Learning with Mixed Noise for Robust Retinal Disease Diagnosis
Previous noise learning methods mainly considered noise arising from images being mislabeled, i. e. label noise, assuming that all mislabeled images are of high image quality.
Domain Adaptation Using Pseudo Labels for COVID-19 Detection
In response to the need for rapid and accurate COVID-19 diagnosis during the global pandemic, we present a two-stage framework that leverages pseudo labels for domain adaptation to enhance the detection of COVID-19 from CT scans.
Advancing COVID-19 Detection in 3D CT Scans
To make a more accurate diagnosis of COVID-19, we propose a straightforward yet effective model.
Retrieval-Augmented Egocentric Video Captioning
In this paper, (1) we develop EgoInstructor, a retrieval-augmented multimodal captioning model that automatically retrieves semantically relevant third-person instructional videos to enhance the video captioning of egocentric videos.
DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images
Computer-assisted automatic analysis of diabetic retinopathy (DR) is of great importance in reducing the risks of vision loss and even blindness.
Learning Open-vocabulary Semantic Segmentation Models From Natural Language Supervision
The former aims to infer all masked entities in the caption given the group tokens, that enables the model to learn fine-grained alignment between visual groups and text entities.
Cross-Field Transformer for Diabetic Retinopathy Grading on Two-field Fundus Images
However, automatic DR grading based on two-field fundus photography remains a challenging task due to the lack of publicly available datasets and effective fusion strategies.
CMC v2: Towards More Accurate COVID-19 Detection with Discriminative Video Priors
This paper presents our solution for the 2nd COVID-19 Competition, occurring in the framework of the AIMIA Workshop at the European Conference on Computer Vision (ECCV 2022).
Boosting COVID-19 Severity Detection with Infection-aware Contrastive Mixup Classification
In our approach, we devise a novel infection-aware 3D Contrastive Mixup Classification network for severity grading.
Deep-OCTA: Ensemble Deep Learning Approaches for Diabetic Retinopathy Analysis on OCTA Images
In the image quality assessment task, we create an ensemble of InceptionV3, SE-ResNeXt, and Vision Transformer models.
FDVTS's Solution for 2nd COV19D Competition on COVID-19 Detection and Severity Analysis
This paper presents our solution for the 2nd COVID-19 Competition, occurring in the framework of the AIMIA Workshop in the European Conference on Computer Vision (ECCV 2022).
CREAM: Weakly Supervised Object Localization via Class RE-Activation Mapping
In this paper, we empirically prove that this problem is associated with the mixup of the activation values between less discriminative foreground regions and the background.
