Medical Diagnosis
213 papers with code • 2 benchmarks • 21 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
DDXPlus
BCI
LIMUC
FracAtlas
Clinical Admission Notes from MIMIC-III
Duke Lung Nodule Dataset 2024
Subtasks
Most implemented papers
BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis
This paper describes the field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening.
Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation
Label shift refers to the phenomenon where the prior class probability p(y) changes between the training and test distributions, while the conditional probability p(x|y) stays fixed.
DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networks
ElementAI/baal
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In this paper, we develop a theoretical framework to approximate Bayesian inference for DNNs by imposing a Bernoulli distribution on the model weights.
D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation
SZUHvern/D-UNet
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This function adds a weighted focal coefficient and combines two traditional loss functions.
A Benchmark of Medical Out of Distribution Detection
However it is unclear which OoDD method should be used in practice.
DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion Models
yqx7150/dp-mdm
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Moreover, virtual binary modal masks are utilized to refine the range of values in k-space data through highly adaptive center windows, which allows the model to focus its attention more efficiently.
Multi-layer Representation Learning for Medical Concepts
Learning efficient representations for concepts has been proven to be an important basis for many applications such as machine translation or document classification.
High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks
In our work, we propose to use a multi-view deep convolutional neural network that handles a set of high-resolution medical images.
TCAV: Relative concept importance testing with Linear Concept Activation Vectors
In particular, this framework enables non-machine learning experts to express concepts of interests and test hypotheses using examples (e. g., a set of pictures that illustrate the concept).
Detecting and Correcting for Label Shift with Black Box Predictors
Faced with distribution shift between training and test set, we wish to detect and quantify the shift, and to correct our classifiers without test set labels.
