Medical Diagnosis
158 papers with code • 2 benchmarks • 15 datasets
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
Benchmarks
These leaderboards are used to track progress in Medical Diagnosis
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Datasets
PadChest
IntrA
BCI
DDXPlus
LIMUC
Clinical Admission Notes from MIMIC-III
FracAtlas
BreastRates4
BreastClassifications4
Subtasks
Latest papers
Convolutional Neural Network Model for Diabetic Retinopathy Feature Extraction and Classification
s21sharan/cnn_dr_detection
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Our contribution is an interpretable model with similar accuracy to more complex models.
Can GPT-4V(ision) Serve Medical Applications? Case Studies on GPT-4V for Multimodal Medical Diagnosis
Driven by the large foundation models, the development of artificial intelligence has witnessed tremendous progress lately, leading to a surge of general interest from the public.
DKEC: Domain Knowledge Enhanced Multi-Label Classification for Electronic Health Records
Multi-label text classification (MLTC) tasks in the medical domain often face long-tail label distribution, where rare classes have fewer training samples than frequent classes.
FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysis
We demonstrate empirically that FairTune leads to improved fairness on a range of medical imaging datasets.
Watch Out! Simple Horizontal Class Backdoors Can Trivially Evade Defenses
shihe98/hcb
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In VCB attacks, any sample from a class activates the implanted backdoor when the secret trigger is present.
BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection
You Only Look Once (YOLO)-based object detectors have shown remarkable accuracy for automated brain tumor detection.
A Theoretical and Practical Framework for Evaluating Uncertainty Calibration in Object Detection
pedrormconde/uncertainty_calibration_object_detection
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For this reason, this work presents a novel theoretical and practical framework to evaluate object detection systems in the context of uncertainty calibration.
Enhancing Network Initialization for Medical AI Models Using Large-Scale, Unlabeled Natural Images
In this study, we explored if SSL for pre-training on non-medical images can be applied to chest radiographs and how it compares to supervised pre-training on non-medical images and on medical images.
RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection
With an excellent balance between speed and accuracy, cutting-edge YOLO frameworks have become one of the most efficient algorithms for object detection.
Defocus Blur Synthesis and Deblurring via Interpolation and Extrapolation in Latent Space
nis-research/linear-latent-blur
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We train autoencoders with implicit and explicit regularization techniques to enforce linearity relations among the representations of different blur levels in the latent space.
