Search Results for author:
Found 8 papers, 2 papers with code
AdaMedGraph: Adaboosting Graph Neural Networks for Personalized Medicine
However, selecting the appropriate edge feature to define patient similarity and construct the graph is challenging, given that each patient is depicted by high-dimensional features from diverse sources.
Beta-Rank: A Robust Convolutional Filter Pruning Method For Imbalanced Medical Image Analysis
We demonstrated that our model performed significantly better than other methods for imbalanced medical datasets.
Ranked #1 on
Medical Image Classification
on IDRiD
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020.
Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning
In this paper, we present a novel Multi-Granularity Cross-modal Alignment (MGCA) framework for generalized medical visual representation learning by harnessing the naturally exhibited semantic correspondences between medical image and radiology reports at three different levels, i. e., pathological region-level, instance-level, and disease-level.
Rethinking Annotation Granularity for Overcoming Shortcuts in Deep Learning-based Radiograph Diagnosis: A Multicenter Study
The models were also compared to radiologists on a subset of the internal testing set (n=496).
Deep Learning based Spectral CT Imaging
To address the image deblurring problem associated with the $L_2^2$-loss, we propose a general $L_p^p$-loss, $p>0$ Furthermore, the images from different energy bins share similar structures of the same object, the regularization characterizing correlations of different energy bins is incorporated into the $L_p^p$-loss function, which helps unify the deep learning based methods with traditional compressed sensing based methods.
Stabilizing Deep Tomographic Reconstruction
ACID synergizes a deep reconstruction network trained on big data, kernel awareness from CS-inspired processing, and iterative refinement to minimize the data residual relative to real measurement.
3D-RADNet: Extracting labels from DICOM metadata for training general medical domain deep 3D convolution neural networks
We applied our method and extracted labels from a large amount of cancer imaging dataset from TCIA to train a medical domain 3D deep convolution neural network.
