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Found 5 papers, 4 papers with code
DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems
While LIME and similar local algorithms have gained popularity due to their simplicity, the random perturbation and feature selection methods result in "instability" in the generated explanations, where for the same prediction, different explanations can be generated.
Transfer Learning with intelligent training data selection for prediction of Alzheimer's Disease
In this paper, we attempt solving these issues with transfer learning, where the state-of-the-art VGG architecture is initialized with pre-trained weights from large benchmark datasets consisting of natural images.
Machine Learning on Biomedical Images: Interactive Learning, Transfer Learning, Class Imbalance, and Beyond
In this paper, we highlight three issues that limit performance of machine learning on biomedical images, and tackle them through 3 case studies: 1) Interactive Machine Learning (IML): we show how IML can drastically improve exploration time and quality of direct volume rendering.
Deep Clustering with a Dynamic Autoencoder: From Reconstruction towards Centroids Construction
In unsupervised learning, there is no apparent straightforward cost function that can capture the significant factors of variations and similarities.
Ranked #1 on
Image Clustering
on MNIST-test
A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation
We propose a generalized focal loss function based on the Tversky index to address the issue of data imbalance in medical image segmentation.
Ranked #1 on
Lesion Segmentation
on BUS 2017 Dataset B
