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 101–125 of 714 papers

TitleStatusHype
Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift AdaptationCode1
Pathological Evidence Exploration in Deep Retinal Image DiagnosisCode1
Hear Your Code Fail, Voice-Assisted Debugging for Python—0
AI-Driven Cytomorphology Image Synthesis for Medical DiagnosticsCode0
AI-Driven MRI-based Brain Tumour Segmentation Benchmarking—0
A Framework for Uncertainty Quantification Based on Nearest Neighbors Across Layers—0
A Real-time Endoscopic Image Denoising System—0
Dr. GPT Will See You Now, but Should It? Exploring the Benefits and Harms of Large Language Models in Medical Diagnosis using Crowdsourced Clinical Cases—0
Taming Stable Diffusion for Computed Tomography Blind Super-Resolution—0
Uncertainty-Aware Deep Learning for Automated Skin Cancer Classification: A Comprehensive Evaluation—0
MedSeg-R: Reasoning Segmentation in Medical Images with Multimodal Large Language Models—0
Stable Vision Concept Transformers for Medical Diagnosis—0
MedOrch: Medical Diagnosis with Tool-Augmented Reasoning Agents for Flexible Extensibility—0
Breaking the Gold Standard: Extracting Forgotten Data under Exact Unlearning in Large Language Models—0
Towards Understanding The Calibration Benefits of Sharpness-Aware Minimization—0
AMQA: An Adversarial Dataset for Benchmarking Bias of LLMs in Medicine and HealthcareCode0
A Comprehensive Survey on the Risks and Limitations of Concept-based Models—0
Predicate-Conditional Conformalized Answer Sets for Knowledge Graph Embeddings—0
A Simple Approximation Algorithm for Optimal Decision Tree—0
Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification—0
Backward Conformal PredictionCode0
Expert-Like Reparameterization of Heterogeneous Pyramid Receptive Fields in Efficient CNNs for Fair Medical Image Classification—0
SMFusion: Semantic-Preserving Fusion of Multimodal Medical Images for Enhanced Clinical Diagnosis—0
A Survey of Slow Thinking-based Reasoning LLMs using Reinforced Learning and Inference-time Scaling Law—0
IP-CRR: Information Pursuit for Interpretable Classification of Chest Radiology ReportsCode0
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Benchmark Results

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