Medical Diagnosis
154 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
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Datasets
PadChest
IntrA
BCI
DDXPlus
LIMUC
Clinical Admission Notes from MIMIC-III
FracAtlas
BreastRates4
BreastClassifications4
Subtasks
Latest papers with no code
MedDr: Diagnosis-Guided Bootstrapping for Large-Scale Medical Vision-Language Learning
The rapid advancement of large-scale vision-language models has showcased remarkable capabilities across various tasks.
Nuclei Instance Segmentation of Cryosectioned H&E Stained Histological Images using Triple U-Net Architecture
The benchmark score for this dataset is AJI 52. 5 and PQ 47. 7, achieved through the implementation of U-Net Architecture.
Self-Supervised Learning Featuring Small-Scale Image Dataset for Treatable Retinal Diseases Classification
The proposed SSL model achieves the state-of-art accuracy of 98. 84% using only 4, 000 training images.
Automated Polyp Segmentation in Colonoscopy Images
The combination of dilated convolution module, RCCA, and global average pooling was found to be effective for irregular shapes.
Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes
In this work, we propose a novel interpretable-by-design method, ProtoVerse, to find relevant sub-parts of vertebral fractures (prototypes) that reliably explain the model's decision in a human-understandable way.
SpikingJET: Enhancing Fault Injection for Fully and Convolutional Spiking Neural Networks
As artificial neural networks become increasingly integrated into safety-critical systems such as autonomous vehicles, devices for medical diagnosis, and industrial automation, ensuring their reliability in the face of random hardware faults becomes paramount.
Parallel Implementations Assessment of a Spatial-Spectral Classifier for Hyperspectral Clinical Applications
While the main constraint for neurosurgical applications is the processing time, in other environments, as the dermatological one, other requirements can be considered.
Learning To Guide Human Decision Makers With Vision-Language Models
As a remedy, we introduce learning to guide (LTG), an alternative framework in which - rather than taking control from the human expert - the machine provides guidance useful for decision making, and the human is entirely responsible for coming up with a decision.
Towards Automatic Evaluation for LLMs' Clinical Capabilities: Metric, Data, and Algorithm
Applying such paradigm, we construct an evaluation benchmark in the field of urology, including a LCP, a SPs dataset, and an automated RAE.
Large Language Models in Biomedical and Health Informatics: A Bibliometric Review
Large Language Models (LLMs) have rapidly become important tools in Biomedical and Health Informatics (BHI), enabling new ways to analyze data, treat patients, and conduct research.
