112 papers with code • 2 benchmarks • 13 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
These leaderboards are used to track progress in Medical Diagnosis
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
In this paper, we develop a theoretical framework to approximate Bayesian inference for DNNs by imposing a Bernoulli distribution on the model weights.
A Benchmark of Medical Out of Distribution Detection
However it is unclear which OoDD method should be used in practice.
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).
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening
We present a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on over 200, 000 exams (over 1, 000, 000 images).
D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation
This function adds a weighted focal coefficient and combines two traditional loss functions.
UnMask: Adversarial Detection and Defense Through Robust Feature Alignment
UnMask detects such attacks and defends the model by rectifying the misclassification, re-classifying the image based on its robust features.