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
| 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
Most implemented papers
Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans
The experimental results show that our automatically searched models (CovidNet3D) outperform the baseline human-designed models on the three datasets with tens of times smaller model size and higher accuracy.
SKID: Self-Supervised Learning for Knee Injury Diagnosis from MRI Data
sadimanna/skid
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The downstream task in our paper is a class imbalanced multi-label classification.
Medical Profile Model: Scientific and Practical Applications in Healthcare
sberbank-ai-lab/mimic.profile
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The paper researches the problem of representation learning for electronic health records.
Learning Optimal Conformal Classifiers
deepmind/conformal_training
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ICLR 2022
However, using CP as a separate processing step after training prevents the underlying model from adapting to the prediction of confidence sets.
RuMedBench: A Russian Medical Language Understanding Benchmark
The paper describes the open Russian medical language understanding benchmark covering several task types (classification, question answering, natural language inference, named entity recognition) on a number of novel text sets.
Reasoning with Language Model Prompting: A Survey
zjunlp/Prompt4ReasoningPapers
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Reasoning, as an essential ability for complex problem-solving, can provide back-end support for various real-world applications, such as medical diagnosis, negotiation, etc.
M$^{2}$SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation
Next, we expand the single-scale SU to the intra-layer multi-scale SU, which can provide the decoder with both pixel-level and structure-level difference information.
Adversarial Feature Map Pruning for Backdoor
retsuh-bqw/fmp
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Unlike existing defense strategies, which focus on reproducing backdoor triggers, FMP attempts to prune backdoor feature maps, which are trained to extract backdoor information from inputs.
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.
Generating Progressive Images from Pathological Transitions via Diffusion Model
rowerliu/adbd
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Deep learning is widely applied in computer-aided pathological diagnosis, which alleviates the pathologist workload and provide timely clinical analysis.
