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Found 9 papers, 4 papers with code
UniChest: Conquer-and-Divide Pre-training for Multi-Source Chest X-Ray Classification
Vision-Language Pre-training (VLP) that utilizes the multi-modal information to promote the training efficiency and effectiveness, has achieved great success in vision recognition of natural domains and shown promise in medical imaging diagnosis for the Chest X-Rays (CXRs).
Combating Representation Learning Disparity with Geometric Harmonization
Self-supervised learning (SSL) as an effective paradigm of representation learning has achieved tremendous success on various curated datasets in diverse scenarios.
Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge
Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" $\unicode{x2013}$ there are a few common findings followed by many more relatively rare conditions.
Bag of Tricks for Long-Tailed Multi-Label Classification on Chest X-Rays
Clinical classification of chest radiography is particularly challenging for standard machine learning algorithms due to its inherent long-tailed and multi-label nature.
Balanced Destruction-Reconstruction Dynamics for Memory-replay Class Incremental Learning
By dynamically manipulating the gradient during training based on these factors, BDR can effectively alleviate knowledge destruction and improve knowledge reconstruction.
Cluster-Guided Unsupervised Domain Adaptation for Deep Speaker Embedding
Inspired by the self-training strategies that use an existing classifier to label the unlabeled data for retraining, we propose a cluster-guided UDA framework that labels the target domain data by clustering and combines the labeled source domain data and pseudo-labeled target domain data to train a speaker embedding network.
Long-Tailed Partial Label Learning via Dynamic Rebalancing
The straightforward combination of LT and PLL, i. e., LT-PLL, suffers from a fundamental dilemma: LT methods build upon a given class distribution that is unavailable in PLL, and the performance of PLL is severely influenced in long-tailed context.
Ferryman as SemEval-2020 Task 5: Optimized BERT for Detecting Counterfactuals
The main purpose of this article is to state the effect of using different methods and models for counterfactual determination and detection of causal knowledge.
Personalized Deep Learning for Ventricular Arrhythmias Detection on Medical IoT Systems
We equip the system with real-time inference on both intracardiac and surface rhythm monitors.
