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Found 27 papers, 14 papers with code
Uncertainty-Aware Adapter: Adapting Segment Anything Model (SAM) for Ambiguous Medical Image Segmentation
The Segment Anything Model (SAM) gained significant success in natural image segmentation, and many methods have tried to fine-tune it to medical image segmentation.
Not just Birds and Cars: Generic, Scalable and Explainable Models for Professional Visual Recognition
Some visual recognition tasks are more challenging then the general ones as they require professional categories of images.
One-Prompt to Segment All Medical Images
Tested on 14 previously unseen datasets, the One-Prompt Model showcases superior zero-shot segmentation capabilities, outperforming a wide range of related methods.
PALM: Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation
Our databases comprises 1200 images with associated labels for the pathologic myopia category and manual annotations of the optic disc, the position of the fovea and delineations of lesions such as patchy retinal atrophy (including peripapillary atrophy) and retinal detachment.
Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation
In Med-SA, we propose Space-Depth Transpose (SD-Trans) to adapt 2D SAM to 3D medical images and Hyper-Prompting Adapter (HyP-Adpt) to achieve prompt-conditioned adaptation.
Simplifying Low-Light Image Enhancement Networks with Relative Loss Functions
In this paper, to make the learning easier in low-light image enhancement, we introduce FLW-Net (Fast and LightWeight Network) and two relative loss functions.
MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer
To effectively integrate these two cutting-edge techniques for the Medical image segmentation, we propose a novel Transformer-based Diffusion framework, called MedSegDiff-V2.
Multi-rater Prism: Learning self-calibrated medical image segmentation from multiple raters
In this paper, we propose a novel neural network framework, called Multi-Rater Prism (MrPrism) to learn the medical image segmentation from multiple labels.
ExpNet: A unified network for Expert-Level Classification
In the paper, we call them expert-level classification.
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model
Inspired by the success of DPM, we propose the first DPM based model toward general medical image segmentation tasks, which we named MedSegDiff.
Learning to screen Glaucoma like the ophthalmologists
GAMMA Challenge is organized to encourage the AI models to screen the glaucoma from a combination of 2D fundus image and 3D optical coherence tomography volume, like the ophthalmologists.
Calibrate the inter-observer segmentation uncertainty via diagnosis-first principle
Inspired by this observation, we propose diagnosis-first principle, which is to take disease diagnosis as the criterion to calibrate the inter-observer segmentation uncertainty.
An Efficient Person Clustering Algorithm for Open Checkout-free Groceries
Then, to ensure that the method adapts to the dynamic and unseen person flow, we propose Graph Convolutional Network (GCN) with a simple Nearest Neighbor (NN) strategy to accurately cluster the instances of CSG.
Dataset and Evaluation algorithm design for GOALS Challenge
Glaucoma causes irreversible vision loss due to damage to the optic nerve, and there is no cure for glaucoma. OCT imaging modality is an essential technique for assessing glaucomatous damage since it aids in quantifying fundus structures.
SeATrans: Learning Segmentation-Assisted diagnosis model via Transformer
To model the segmentation-diagnosis interaction, SeA-block first embeds the diagnosis feature based on the segmentation information via the encoder, and then transfers the embedding back to the diagnosis feature space by a decoder.
Learning self-calibrated optic disc and cup segmentation from multi-rater annotations
In this paper, we propose a novel neural network framework to learn OD/OC segmentation from multi-rater annotations.
One Hyper-Initializer for All Network Architectures in Medical Image Analysis
Pre-training is essential to deep learning model performance, especially in medical image analysis tasks where limited training data are available.
Contrastive Centroid Supervision Alleviates Domain Shift in Medical Image Classification
Deep learning based medical imaging classification models usually suffer from the domain shift problem, where the classification performance drops when training data and real-world data differ in imaging equipment manufacturer, image acquisition protocol, patient populations, etc.
REFUGE2 Challenge: A Treasure Trove for Multi-Dimension Analysis and Evaluation in Glaucoma Screening
Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge -- Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2).
Opinions Vary? Diagnosis First!
Motivated by the observation that OD/OC segmentation is often used for the glaucoma diagnosis clinically, in this paper, we propose a novel strategy to fuse the multi-rater OD/OC segmentation labels via the glaucoma diagnosis performance.
GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges
However, although numerous algorithms are proposed based on fundus images or OCT volumes in computer-aided diagnosis, there are still few methods leveraging both of the modalities for the glaucoma assessment.
Progressive Hard-case Mining across Pyramid Levels for Object Detection
Based on focal loss with ATSS-R50, our approach achieves 40. 5 AP, surpassing the state-of-the-art QFL (Quality Focal Loss, 39. 9 AP) and VFL (Varifocal Loss, 40. 1 AP).
Learning Calibrated Medical Image Segmentation via Multi-Rater Agreement Modeling
To our knowledge, our work is the first in producing calibrated predictions under different expertise levels for medical image segmentation.
TR-GAN: Topology Ranking GAN with Triplet Loss for Retinal Artery/Vein Classification
A topology ranking discriminator based on ordinal regression is proposed to rank the topological connectivity level of the ground-truth, the generated A/V mask and the intentionally shuffled mask.
Leveraging Undiagnosed Data for Glaucoma Classification with Teacher-Student Learning
Recently, deep learning has been adopted to the glaucoma classification task with performance comparable to that of human experts.
Universal, transferable and targeted adversarial attacks
The question is: Is there existan attack that can meet all these requirements?
Integrating neural networks into the blind deblurring framework to compete with the end-to-end learning-based methods
Recently, end-to-end learning-based methods based on deep neural network (DNN) have been proven effective for blind deblurring.
