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Found 23 papers, 11 papers with code
APPLE: Adversarial Privacy-aware Perturbations on Latent Embedding for Unfairness Mitigation
Ensuring fairness in deep-learning-based segmentors is crucial for health equity.
CARZero: Cross-Attention Alignment for Radiology Zero-Shot Classification
The advancement of Zero-Shot Learning in the medical domain has been driven forward by using pre-trained models on large-scale image-text pairs, focusing on image-text alignment.
ECAMP: Entity-centered Context-aware Medical Vision Language Pre-training
Despite significant advancements in medical vision-language pre-training, existing methods have largely overlooked the inherent entity-specific context within radiology reports and the complex cross-modality contextual relationships between text and images.
Adversarial Medical Image with Hierarchical Feature Hiding
Interestingly, this vulnerability is a double-edged sword, which can be exploited to hide AEs.
Long-tailed multi-label classification with noisy label of thoracic diseases from chest X-ray
This work establishes a foundation for robust CAD methods, achieving a balance in identifying a spectrum of thoracic diseases in CXRs.
Stochastic Learning of Semiparametric Monotone Index Models with Large Sample Size
My proposed algorithm essentially generalizes MBGD algorithm to the semiparametric setup.
UOD: Universal One-shot Detection of Anatomical Landmarks
However, existing one-shot learning methods are highly specialized in a single domain and suffer domain preference heavily in the situation of multi-domain unlabeled data.
Unsupervised augmentation optimization for few-shot medical image segmentation
The augmentation parameters matter to few-shot semantic segmentation since they directly affect the training outcome by feeding the networks with varying perturbated samples.
FairAdaBN: Mitigating unfairness with adaptive batch normalization and its application to dermatological disease classification
Deep learning is becoming increasingly ubiquitous in medical research and applications while involving sensitive information and even critical diagnosis decisions.
Information-guided pixel augmentation for pixel-wise contrastive learning
To the best of our knowledge, we are the first to propose a pixel augmentation method with a pixel granularity for enhancing unsupervised pixel-wise contrastive learning.
Fairness in Medical Image Analysis and Healthcare: A Literature Survey
Machine learning-enabled medical imaging analysis has become a vital part of the automatic diagnosis system.
DATR: Domain-adaptive transformer for multi-domain landmark detection
In this work, we propose a universal model for multi-domain landmark detection by taking advantage of transformer for modeling long dependencies and develop a domain-adaptive transformer model, named as DATR, which is trained on multiple mixed datasets from different anatomies and capable of detecting landmarks of any image from those anatomies.
Rib Suppression in Digital Chest Tomosynthesis
Digital chest tomosynthesis (DCT) is a technique to produce sectional 3D images of a human chest for pulmonary disease screening, with 2D X-ray projections taken within an extremely limited range of angles.
Relative distance matters for one-shot landmark detection
Contrastive learning based methods such as cascade comparing to detect (CC2D) have shown great potential for one-shot medical landmark detection.
Which images to label for few-shot medical landmark detection?
We herein propose a novel Sample Choosing Policy (SCP) to select "the most worthy" images for annotation, in the context of few-shot medical landmark detection.
Medical Aegis: Robust adversarial protectors for medical images
To the best of our knowledge, Medical Aegis is the first defense in the literature that successfully addresses the strong adaptive adversarial example attacks to medical images.
Estimating High Dimensional Monotone Index Models by Iterative Convex Optimization1
For such monotone index models with increasing dimension, we propose to use a new class of estimators based on batched gradient descent (BGD) involving nonparametric methods such as kernel estimation or sieve estimation, and study their asymptotic properties.
One-Shot Medical Landmark Detection
The success of deep learning methods relies on the availability of a large number of datasets with annotations; however, curating such datasets is burdensome, especially for medical images.
You Only Learn Once: Universal Anatomical Landmark Detection
However, all of those methods are unary in the sense that a highly specialized network is trained for a single task say associated with a particular anatomical region.
A Hierarchical Feature Constraint to Camouflage Medical Adversarial Attacks
Deep neural networks (DNNs) for medical images are extremely vulnerable to adversarial examples (AEs), which poses security concerns on clinical decision making.
Label-Free Segmentation of COVID-19 Lesions in Lung CT
Scarcity of annotated images hampers the building of automated solution for reliable COVID-19 diagnosis and evaluation from CT. To alleviate the burden of data annotation, we herein present a label-free approach for segmenting COVID-19 lesions in CT via pixel-level anomaly modeling that mines out the relevant knowledge from normal CT lung scans.
Miss the Point: Targeted Adversarial Attack on Multiple Landmark Detection
A comprehensive evaluation on a public dataset for cephalometric landmark detection demonstrates that the adversarial examples generated by ATI-FGSM break the CNN-based network more effectively and efficiently, compared with the original Iterative FGSM attack.
3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation
Instead of a collection of multiple models, it is highly desirable to learn a universal data representation for different tasks, ideally a single model with the addition of a minimal number of parameters steered to each task.
