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Found 21 papers, 6 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.
Inspecting Model Fairness in Ultrasound Segmentation Tasks
With the rapid expansion of machine learning and deep learning (DL), researchers are increasingly employing learning-based algorithms to alleviate diagnostic challenges across diverse medical tasks and applications.
MobileUtr: Revisiting the relationship between light-weight CNN and Transformer for efficient medical image segmentation
This work revisits the relationship between CNNs and Transformers in lightweight universal networks for medical image segmentation, aiming to integrate the advantages of both worlds at the infrastructure design level.
Slide-SAM: Medical SAM Meets Sliding Window
To address these problems, we propose Slide-SAM, which treats a stack of three adjacent slices as a prediction window.
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.
GDDS: Pulmonary Bronchioles Segmentation with Group Deep Dense Supervision
To address these problems, we contribute a new bronchial segmentation method based on Group Deep Dense Supervision (GDDS) that emphasizes fine-scale bronchioles segmentation in a simple-but-effective manner.
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.
Recovering medical images from CT film photos
While medical images such as computed tomography (CT) are stored in DICOM format in hospital PACS, it is still quite routine in many countries to print a film as a transferable medium for the purposes of self-storage and secondary consultation.
Universal Segmentation of 33 Anatomies
Firstly, while it is ideal to learn such a model from a large-scale, fully-annotated dataset, it is practically hard to curate such a dataset.
MixCL: Pixel label matters to contrastive learning
It is essential for medical image analysis, which is often notorious for its lack of annotations.
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.
Where is the disease? Semi-supervised pseudo-normality synthesis from an abnormal image
Pseudo-normality synthesis, which computationally generates a pseudo-normal image from an abnormal one (e. g., with lesions), is critical in many perspectives, from lesion detection, data augmentation to clinical surgery suggestion.
CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography
Spine-related diseases have high morbidity and cause a huge burden of social cost.
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.
CT Film Recovery via Disentangling Geometric Deformation and Illumination Variation: Simulated Datasets and Deep Models
We also record all accompanying information related to the geometric deformation (such as 3D coordinate, depth, normal, and UV maps) and illumination variation (such as albedo map).
An Efficient Target Detection and Recognition Method in Aerial Remote-sensing Images Based on Multiangle Regions-of-Interest
Recently, deep learning technology have been extensively used in the field of image recognition.
Calibration of Multiple Fish-Eye Cameras Using a Wand
Fish-eye cameras are becoming increasingly popular in computer vision, but their use for 3D measurement is limited partly due to the lack of an accurate, efficient and user-friendly calibration procedure.
A New Continuous-Time Equality-Constrained Optimization Method to Avoid Singularity
To avoid such a singularity, we propose a new projection matrix, based on which a feasible point method for the continuous-time, equality-constrained optimization problem is developed.
