SOTAVerified

Medical Diagnosis

Medical Diagnosis is the process of identifying the disease a patient is affected by, based on the assessment of specific risk factors, signs, symptoms and results of exams.

Source: A probabilistic network for the diagnosis of acute cardiopulmonary diseases

Papers

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Showing 376–400 of 714 papers

TitleStatusHype
Consecutive Knowledge Meta-Adaptation Learning for Unsupervised Medical Diagnosis—0
Weakly Supervised Medical Image Segmentation With Soft Labels and Noise Robust Loss—0
Deep Labeling of fMRI Brain Networks Using Cloud Based Processing—0
Chromosome Segmentation Analysis Using Image Processing Techniques and Autoencoders—0
Robust and Efficient Imbalanced Positive-Unlabeled Learning with Self-supervisionCode0
Robustness of an Artificial Intelligence Solution for Diagnosis of Normal Chest X-Rays—0
Automated recognition of the pericardium contour on processed CT images using genetic algorithms—0
TESTSGD: Interpretable Testing of Neural Networks Against Subtle Group Discrimination—0
Automatic Ultrasound Image Segmentation of Supraclavicular Nerve Using Dilated U-Net Deep Learning Architecture—0
Slice-level Detection of Intracranial Hemorrhage on CT Using Deep Descriptors of Adjacent Slices—0
Adaptive Temperature Scaling for Robust Calibration of Deep Neural Networks—0
Applied Computer Vision on 2-Dimensional Lung X-Ray Images for Assisted Medical Diagnosis of Pneumonia—0
Learning Relaxation for Multigrid—0
Do uHear? Validation of uHear App for Preliminary Screening of Hearing Ability in Soundscape StudiesCode0
Revealing Unfair Models by Mining Interpretable Evidence—0
Identifying the Context Shift between Test Benchmarks and Production Data—0
Fairness-aware Model-agnostic Positive and Unlabeled Learning—0
An Improved Deep Convolutional Neural Network by Using Hybrid Optimization Algorithms to Detect and Classify Brain Tumor Using Augmented MRI ImagesCode0
Cardiomegaly Detection using Deep Convolutional Neural Network with U-Net—0
Federated learning: Applications, challenges and future directions—0
Self-Supervised Masking for Unsupervised Anomaly Detection and Localization—0
Incorporating Medical Knowledge to Transformer-based Language Models for Medical Dialogue Generation—0
Refining Diagnosis Paths for Medical Diagnosis based on an Augmented Knowledge Graph—0
Neurochaos Feature Transformation and Classification for Imbalanced LearningCode0
Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DenseNet-161Average Precision0.74—Unverified