Search Results for author:
Found 31 papers, 12 papers with code
Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks
It covers more than 200 papers from three key perspectives: 1) input explainability through the integration of explainable feature engineering and knowledge graph, 2) model explainability via attention-based learning, concept-based learning, and prototype-based learning, and 3) output explainability by providing counterfactual explanation and textual explanation.
ReXplain: Translating Radiology into Patient-Friendly Video Reports
Radiology reports often remain incomprehensible to patients, undermining patient-centered care.
SurgPETL: Parameter-Efficient Image-to-Surgical-Video Transfer Learning for Surgical Phase Recognition
Capitalizing on image-level pre-trained models for various downstream tasks has recently emerged with promising performance.
Towards Non-invasive and Personalized Management of Breast Cancer Patients from Multiparametric MRI via A Large Mixture-of-Modality-Experts Model
MOME exemplifies a discriminative, robust, scalable, and interpretable multimodal model, paving the way for noninvasive, personalized management of breast cancer patients based on multiparametric breast imaging data.
Surgformer: Surgical Transformer with Hierarchical Temporal Attention for Surgical Phase Recognition
Moreover, we propose a novel Hierarchical Temporal Attention (HTA) to capture both global and local information within varied temporal resolutions from a target frame-centric perspective.
Enable the Right to be Forgotten with Federated Client Unlearning in Medical Imaging
In this paper, we delve into the paradigm of Federated Client Unlearning (FCU) to guarantee a client the right to erase the contribution or the influence, introducing the first FU framework in medical imaging.
Multimodal Data Integration for Precision Oncology: Challenges and Future Directions
The essence of precision oncology lies in its commitment to tailor targeted treatments and care measures to each patient based on the individual characteristics of the tumor.
Dia-LLaMA: Towards Large Language Model-driven CT Report Generation
Medical report generation has achieved remarkable advancements yet has still been faced with several challenges.
XCoOp: Explainable Prompt Learning for Computer-Aided Diagnosis via Concept-guided Context Optimization
Utilizing potent representations of the large vision-language models (VLMs) to accomplish various downstream tasks has attracted increasing attention.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Medical Image Debiasing by Learning Adaptive Agreement from a Biased Council
Specifically, the debiasing model is required to learn adaptive agreement with the biased council by agreeing on the correctly predicted samples and disagreeing on the wrongly predicted samples by the biased council.
MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept Alignment
Black-box deep learning approaches have showcased significant potential in the realm of medical image analysis.
Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Bag-Level Classifier is a Good Instance-Level Teacher
Based on this idea, we design Iteratively Coupled Multiple Instance Learning (ICMIL) to couple the embedder and the bag classifier at a low cost.
Iterative Semi-Supervised Learning for Abdominal Organs and Tumor Segmentation
Initially, a deep model (nn-UNet) trained on datasets with complete organ annotations (about 220 scans) generates pseudo labels for the whole dataset.
Deep Omni-supervised Learning for Rib Fracture Detection from Chest Radiology Images
To tackle this challenge, the literature on object detection has witnessed an increase of weakly-supervised and semi-supervised approaches, yet still lacks a unified framework that leverages various forms of fully-labeled, weakly-labeled, and unlabeled data.
Advancing Volumetric Medical Image Segmentation via Global-Local Masked Autoencoder
Masked autoencoder (MAE) is a promising self-supervised pre-training technique that can improve the representation learning of a neural network without human intervention.
Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images
To guide the segmentation branch to learn from richer high-resolution features, we propose a feature affinity module and a scale affinity module to enhance the multi-task learning of the dual branches.
Scale Federated Learning for Label Set Mismatch in Medical Image Classification
Federated learning (FL) has been introduced to the healthcare domain as a decentralized learning paradigm that allows multiple parties to train a model collaboratively without privacy leakage.
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.
Learning Robust Medical Image Segmentation from Multi-source Annotations
Learning segmentation networks from multi-source annotations remains a challenge due to the uncertainties brought by the variance of annotations and the quality of images.
Iteratively Coupled Multiple Instance Learning from Instance to Bag Classifier for Whole Slide Image Classification
In ICMIL, we use category information in the bag-level classifier to guide the patch-level fine-tuning of the patch feature extractor.
Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions
Thus, label-efficient deep learning methods are developed to make comprehensive use of the labeled data as well as the abundance of unlabeled and weak-labeled data.
ORF-Net: Deep Omni-supervised Rib Fracture Detection from Chest CT Scans
In this paper, we proposed a novel omni-supervised object detection network, which can exploit multiple different forms of annotated data to further improve the detection performance.
Pseudo Bias-Balanced Learning for Debiased Chest X-ray Classification
Deep learning models were frequently reported to learn from shortcuts like dataset biases.
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).
OXnet: Omni-supervised Thoracic Disease Detection from Chest X-rays
Then, we inject a global classification head to the detection model and propose dual attention alignment to guide the global gradient to the local detection branch, which enables learning lesion detection from image-level annotations.
Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images
The novel coronavirus disease 2019 (COVID-19) characterized by atypical pneumonia has caused millions of deaths worldwide.
Deep Semi-supervised Metric Learning with Dual Alignment for Cervical Cancer Cell Detection
Specifically, our model learns a metric space and conducts dual alignment of semantic features on both the proposal level and the prototype levels.
Deep Mining External Imperfect Data for Chest X-ray Disease Screening
Recent researches have demonstrated that performance bottleneck exists in joint training on different CXR datasets, and few made efforts to address the obstacle.
Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model
It is a consistency-based method which exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations, and leverages a self-ensembling model to produce high-quality consistency targets for the unlabeled data.
Unifying Structure Analysis and Surrogate-driven Function Regression for Glaucoma OCT Image Screening
Besides, the proposed multi-task learning network is capable of exploring the structure and function relationship from the OCT image and visual field measurement simultaneously, which contributes to classification performance boosting.
Deep Angular Embedding and Feature Correlation Attention for Breast MRI Cancer Analysis
In this paper, we propose to identify breast tumor in MRI by Cosine Margin Sigmoid Loss (CMSL) with deep learning (DL) and localize possible cancer lesion by COrrelation Attention Map (COAM) based on the learned features.
