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Found 30 papers, 17 papers with code
Partially Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation
Specifically, our framework consists of a compact segmentation network with modality specific normalization layers for learning with partially labeled unpaired multi-modal data.
BenchX: A Unified Benchmark Framework for Medical Vision-Language Pretraining on Chest X-Rays
Medical Vision-Language Pretraining (MedVLP) shows promise in learning generalizable and transferable visual representations from paired and unpaired medical images and reports.
Enhancing Community Vision Screening -- AI Driven Retinal Photography for Early Disease Detection and Patient Trust
Our study employs four distinct deep learning models: RETinal photo Quality Assessment (RETQA), Pathology Visual Impairment detection (PVI), Eye Disease Diagnosis (EDD) and Visualization of Lesion Regions of the eye (VLR).
A New Perspective to Boost Performance Fairness for Medical Federated Learning
However, existing fair FL methods ignore the specific characteristics of medical FL applications, i. e., domain shift among the datasets from different hospitals.
UrFound: Towards Universal Retinal Foundation Models via Knowledge-Guided Masked Modeling
Retinal foundation models aim to learn generalizable representations from diverse retinal images, facilitating label-efficient model adaptation across various ophthalmic tasks.
CPT: Consistent Proxy Tuning for Black-box Optimization
This consistency benefits Proxy-tuning and enhances model performance.
Clinical Domain Knowledge-Derived Template Improves Post Hoc AI Explanations in Pneumothorax Classification
We anticipate that our template guidance will forge a fresh approach to elucidating AI models by integrating clinical domain expertise.
RLPeri: Accelerating Visual Perimetry Test with Reinforcement Learning and Convolutional Feature Extraction
During the test, a patient's gaze is fixed at a specific location while light stimuli of varying intensities are presented in central and peripheral vision.
Learning Prompt with Distribution-Based Feature Replay for Few-Shot Class-Incremental Learning
When progressing to a new session, pseudo-features are sampled from old-class distributions combined with training images of the current session to optimize the prompt, thus enabling the model to learn new knowledge while retaining old knowledge.
class-incremental learning
Few-Shot Class-Incremental Learning
+2
VQA4CIR: Boosting Composed Image Retrieval with Visual Question Answering
By feeding the retrieved image and question to the VQA model, one can find the images inconsistent with relative caption when the answer by VQA is inconsistent with the answer in the QA pair.
Leveraging Anatomical Constraints with Uncertainty for Pneumothorax Segmentation
We propose a novel approach that incorporates the lung+ space as a constraint during DL model training for pneumothorax segmentation on 2D chest radiographs.
A Surrogate-Assisted Extended Generative Adversarial Network for Parameter Optimization in Free-Form Metasurface Design
Metasurfaces have widespread applications in fifth-generation (5G) microwave communication.
Sentence-level Prompts Benefit Composed Image Retrieval
Composed image retrieval (CIR) is the task of retrieving specific images by using a query that involves both a reference image and a relative caption.
Ranked #2 on
Image Retrieval
on Fashion IQ
(using extra training data)
DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost Volume
Nevertheless, the dynamic cost volume inevitably generates extra occlusions and noise, thus we alleviate this by designing a fusion module that makes static and dynamic cost volumes compensate for each other.
Towards Instance-adaptive Inference for Federated Learning
However, the performance of the global model is often hampered by non-i. i. d.
Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification
Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classification of retinal anomalies.
Learning Federated Visual Prompt in Null Space for MRI Reconstruction
Federated Magnetic Resonance Imaging (MRI) reconstruction enables multiple hospitals to collaborate distributedly without aggregating local data, thereby protecting patient privacy.
Federated Uncertainty-Aware Aggregation for Fundus Diabetic Retinopathy Staging
Our TWEU employs an evidential deep layer to produce the uncertainty score with the DR staging results for client reliability evaluation.
Medical Phrase Grounding with Region-Phrase Context Contrastive Alignment
To enable MedRPG to locate nuanced medical findings with better region-phrase correspondences, we further propose Tri-attention Context contrastive alignment (TaCo).
Localizing Anatomical Landmarks in Ocular Images using Zoom-In Attentive Networks
Therefore, localization has its unique challenges different from segmentation or detection.
CRAFT: Cross-Attentional Flow Transformer for Robust Optical Flow
This is mainly because the correlation volume, the basis of pixel matching, is computed as the dot product of the convolutional features of the two images.
Ranked #10 on
Optical Flow Estimation
on KITTI 2015 (train)
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).
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.
Few-Shot Domain Adaptation with Polymorphic Transformers
Thus, the chance of overfitting the annotations is greatly reduced, and the model can perform robustly on the target domain after being trained on a few annotated images.
Medical Image Segmentation Using Squeeze-and-Expansion Transformers
Medical image segmentation is important for computer-aided diagnosis.
Ranked #1 on
Brain Tumor Segmentation
on BRATS 2019
Crowd Counting With Partial Annotations in an Image
To fully leverage the data captured from different scenes with different view angles while reducing the annotation cost, this paper studies a novel crowd counting setting, i. e. only using partial annotations in each image as training data.
Laplacian-Steered Neural Style Transfer
However in the traditional optimization objective, low-level features of the content image are absent, and the low-level features of the style image dominate the low-level detail structures of the new image.
Improved Recurrent Neural Networks for Session-based Recommendations
Recurrent neural networks (RNNs) were recently proposed for the session-based recommendation task.
Transfer Hashing with Privileged Information
Specifically, we extend the standard learning to hash method, Iterative Quantization (ITQ), in a transfer learning manner, namely ITQ+.
Simple and Efficient Learning using Privileged Information
The Support Vector Machine using Privileged Information (SVM+) has been proposed to train a classifier to utilize the additional privileged information that is only available in the training phase but not available in the test phase.
