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Found 90 papers, 44 papers with code
Stealthy Patch-Wise Backdoor Attack in 3D Point Cloud via Curvature Awareness
Backdoor attacks pose a severe threat to deep neural networks (DNN) by implanting hidden backdoors that can be activated with predefined triggers to manipulate model behaviors maliciously.
MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and Retention
MIRROR employs dedicated encoders to extract comprehensive features for each modality, which is further complemented by a modality alignment module to achieve seamless integration between phenotype patterns and molecular profiles.
Difference vs. Quotient: A Novel Algorithm for Dominant Eigenvalue Problem
Within this family, we propose the Split-Merge algorithm, which achieves maximal acceleration without spectral prior knowledge and operates solely through matrix-vector products, making it both efficient and easy to implement.
FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis
Ignoring this imbalance can lead the global model's generalization ability to be sub-optimal, further limiting the novel domain generation procedure.
Meta Curvature-Aware Minimization for Domain Generalization
However, SAM and its variants sometimes fail to guide the model toward a flat minimum, and their training processes exhibit limitations, hindering further improvements in model generalization.
Breaking the Cycle of Recurring Failures: Applying Generative AI to Root Cause Analysis in Legacy Banking Systems
Traditional banks face significant challenges in digital transformation, primarily due to legacy system constraints and fragmented ownership.
A Survey of Medical Vision-and-Language Applications and Their Techniques
Unlike general vision-and-language models trained on diverse, non-specialized datasets, MVLMs are purpose-built for the medical domain, automatically extracting and interpreting critical information from medical images and textual reports to support clinical decision-making.
CoSAM: Self-Correcting SAM for Domain Generalization in 2D Medical Image Segmentation
Existing SAM-based approaches attempt to address the need for manual prompts by introducing prompt generators that automatically generate these prompts.
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain.
Day-Night Adaptation: An Innovative Source-free Adaptation Framework for Medical Image Segmentation
To facilitate adaptation while preserving data privacy, source-free domain adaptation (SFDA) and test-time adaptation (TTA) have emerged as effective paradigms, relying solely on target domain data.
MedUniSeg: 2D and 3D Medical Image Segmentation via a Prompt-driven Universal Model
In this paper, we introduce MedUniSeg, a prompt-driven universal segmentation model designed for 2D and 3D multi-task segmentation across diverse modalities and domains.
Pre-training Everywhere: Parameter-Efficient Fine-Tuning for Medical Image Analysis via Target Parameter Pre-training
During this stage, the pre-trained backbone parameters are frozen, and only the target parameters are trainable.
From Few to More: Scribble-based Medical Image Segmentation via Masked Context Modeling and Continuous Pseudo Labels
MaCo employs masked context modeling (MCM) and continuous pseudo labels (CPL).
Gradient Alignment Improves Test-Time Adaptation for Medical Image Segmentation
Extensive experiments establish the effectiveness of the proposed gradient alignment and dynamic learning rate and substantiate the superiority of our GraTa method over other state-of-the-art TTA methods on a benchmark medical image segmentation task.
Unleashing the Potential of Open-set Noisy Samples Against Label Noise for Medical Image Classification
Addressing mixed closed-set and open-set label noise in medical image classification remains a largely unexplored challenge.
Towards Clinician-Preferred Segmentation: Leveraging Human-in-the-Loop for Test Time Adaptation in Medical Image Segmentation
Extensive experiments on a public dataset underscore the superiority of our HiTTA over existing TTA methods, emphasizing the advantages of integrating human feedback and our divergence loss in enhancing the model's performance and adaptability across diverse medical centers.
PairAug: What Can Augmented Image-Text Pairs Do for Radiology?
Acknowledging this limitation, our objective is to devise a framework capable of concurrently augmenting medical image and text data.
Block Sparse Bayesian Learning: A Diversified Scheme
This paper introduces a novel prior called Diversified Block Sparse Prior to characterize the widespread block sparsity phenomenon in real-world data.
MAST: Video Polyp Segmentation with a Mixture-Attention Siamese Transformer
To the best of our knowledge, our MAST is the first transformer model dedicated to video polyp segmentation.
SurgicalPart-SAM: Part-to-Whole Collaborative Prompting for Surgical Instrument Segmentation
Specifically, we achieve this by proposing (1) Collaborative Prompts that describe instrument structures via collaborating category-level and part-level texts; (2) Cross-Modal Prompt Encoder that encodes text prompts jointly with visual embeddings into discriminative part-level representations; and (3) Part-to-Whole Adaptive Fusion and Hierarchical Decoding that adaptively fuse the part-level representations into a whole for accurate instrument segmentation in surgical scenarios.
SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis.
Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts
Extensive experiments and analysis on five real multi-center medical image datasets demonstrate the superiority of FEAL over the state-of-the-art active learning methods in federated scenarios with domain shifts.
Each Test Image Deserves A Specific Prompt: Continual Test-Time Adaptation for 2D Medical Image Segmentation
Distribution shift widely exists in medical images acquired from different medical centres and poses a significant obstacle to deploying the pre-trained semantic segmentation model in real-world applications.
Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation Learning
In this paper, we reconsider versatile self-supervised learning from the perspective of continual learning and propose MedCoSS, a continuous self-supervised learning approach for multi-modal medical data.
UAE: Universal Anatomical Embedding on Multi-modality Medical Images
They use self-supervised learning to acquire a discriminative embedding for each voxel within the image.
Segment Together: A Versatile Paradigm for Semi-Supervised Medical Image Segmentation
Therefore, in this paper, we introduce a \textbf{Ver}satile \textbf{Semi}-supervised framework (VerSemi) to point out a new perspective that integrates various tasks into a unified model with a broad label space, to exploit more unlabeled data for semi-supervised medical image segmentation.
Discrepancy Matters: Learning from Inconsistent Decoder Features for Consistent Semi-supervised Medical Image Segmentation
Semi-supervised learning (SSL) has been proven beneficial for mitigating the issue of limited labeled data especially on the task of volumetric medical image segmentation.
Tackling the Incomplete Annotation Issue in Universal Lesion Detection Task By Exploratory Training
Pseudo-label-based methods examine the training data and mine unlabelled objects for retraining, which have shown to be effective to tackle this issue.
URL: Combating Label Noise for Lung Nodule Malignancy Grading
Although researchers adopt the label-noise-robust methods to handle label noise for lung nodule malignancy grading, they do not consider the inherent ordinal relation among classes of this task.
SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation
However, we observe two problems with this naive pipeline: (1) the domain gap between natural objects and surgical instruments leads to inferior generalisation of SAM; and (2) SAM relies on precise point or box locations for accurate segmentation, requiring either extensive manual guidance or a well-performing specialist detector for prompt preparation, which leads to a complex multi-stage pipeline.
The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT
Overall KiTS21 facilitated a significant advancement in the state of the art in kidney tumor segmentation, and provides useful insights that are applicable to the field of semantic segmentation as a whole.
SAM++: Enhancing Anatomic Matching using Semantic Information and Structural Inference
Medical images like CT and MRI provide detailed information about the internal structure of the body, and identifying key anatomical structures from these images plays a crucial role in clinical workflows.
Devil is in Channels: Contrastive Single Domain Generalization for Medical Image Segmentation
In C$^2$SDG, the shallower features of each image and its style-augmented counterpart are extracted and used for contrastive training, resulting in the disentangled style representations and structure representations.
TEC-Net: Vision Transformer Embrace Convolutional Neural Networks for Medical Image Segmentation
The hybrid architecture of convolution neural networks (CNN) and Transformer has been the most popular method for medical image segmentation.
Transformer-based Annotation Bias-aware Medical Image Segmentation
This paper proposes a Transformer-based Annotation Bias-aware (TAB) medical image segmentation model, which tackles the annotator-related bias via modeling annotator preference and stochastic errors.
Treasure in Distribution: A Domain Randomization based Multi-Source Domain Generalization for 2D Medical Image Segmentation
In this paper, we propose a multi-source DG method called Treasure in Distribution (TriD), which constructs an unprecedented search space to obtain the model with strong robustness by randomly sampling from a uniform distribution.
Attention Mechanisms in Medical Image Segmentation: A Survey
This paper systematically reviews the basic principles of attention mechanisms and their applications in medical image segmentation.
Reconstruction-driven Dynamic Refinement based Unsupervised Domain Adaptation for Joint Optic Disc and Cup Segmentation
The reconstruction alignment (RA) module uses a variational auto-encoder (VAE) to reconstruct the input image and thus boosts the image representation ability of the network in a self-supervised way.
UniSeg: A Prompt-driven Universal Segmentation Model as well as A Strong Representation Learner
Moreover, UniSeg also beats other pre-trained models on two downstream datasets, providing the community with a high-quality pre-trained model for 3D medical image segmentation.
An End-to-End Framework For Universal Lesion Detection With Missing Annotations
In this work, we present a novel end-to-end framework for mining unlabeled lesions while simultaneously training the detector.
DAN-NucNet: A dual attention based framework for nuclei segmentation in cancer histology images under wild clinical conditions
The nuclei segmentation in histology images is challenging in variable conditions (clinical wild), such as poor staining quality, stain variability, tissue variability, and conditions having higher morphological variability.
PEFAT: Boosting Semi-Supervised Medical Image Classification via Pseudo-Loss Estimation and Feature Adversarial Training
In this paper, we propose a novel Pseudo-loss Estimation and Feature Adversarial Training semi-supervised framework, termed as PEFAT, to boost the performance of multi-class and multi-label medical image classification from the point of loss distribution modeling and adversarial training.
Image Classification
Semi-supervised Medical Image Classification
Instance-dependent Label Distribution Estimation for Learning with Label Noise
These methods heavily rely on the existence of anchor points or the quality of pseudo ones, and the global NTM can hardly provide accurate label transition information for each sample, since the label noise in real applications is mostly instance-dependent.
ProSFDA: Prompt Learning based Source-free Domain Adaptation for Medical Image Segmentation
In this paper, we propose a \textbf{Pro}mpt learning based \textbf{SFDA} (\textbf{ProSFDA}) method for medical image segmentation, which aims to improve the quality of domain adaption by minimizing explicitly the domain discrepancy.
Learning from partially labeled data for multi-organ and tumor segmentation
To address this, we propose a Transformer based dynamic on-demand network (TransDoDNet) that learns to segment organs and tumors on multiple partially labeled datasets.
Boundary-Aware Network for Kidney Parsing
Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate segmentation of kidney structures on computed tomography angiography (CTA) images remains challenging, due to the variable sizes of kidney tumors and the ambiguous boundaries between kidney structures and their surroundings.
Boundary-Aware Network for Abdominal Multi-Organ Segmentation
In this paper, we propose a boundary-aware network (BA-Net) to segment abdominal organs on CT scans and MRI scans.
Label Propagation for 3D Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis
Carotid vessel wall segmentation is a crucial yet challenging task in the computer-aided diagnosis of atherosclerosis.
ClusTR: Exploring Efficient Self-attention via Clustering for Vision Transformers
Besides, we further extend the clustering-guided attention from single-scale to multi-scale, which is conducive to dense prediction tasks.
HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging
In our previous work, $i. e.$, HNF-Net, high-resolution feature representation and light-weight non-local self-attention mechanism are exploited for brain tumor segmentation using multi-modal MR imaging.
MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images
Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment.
UniMiSS: Universal Medical Self-Supervised Learning via Breaking Dimensionality Barrier
In this paper, we advocate bringing a wealth of 2D images like chest X-rays as compensation for the lack of 3D data, aiming to build a universal medical self-supervised representation learning framework, called UniMiSS.
Dual-Flow Transformation Network for Deformable Image Registration with Region Consistency Constraint
In this paper, we present a novel dual-flow transformation network with region consistency constraint which maximizes the similarity of ROIs within a pair of images and estimates both global and region spatial transformations simultaneously.
FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis
However, designing a unified BA method that can be applied to various MIA systems is challenging due to the diversity of imaging modalities (e. g., X-Ray, CT, and MRI) and analysis tasks (e. g., classification, detection, and segmentation).
Modeling Annotator Preference and Stochastic Annotation Error for Medical Image Segmentation
Manual annotation of medical images is highly subjective, leading to inevitable and huge annotation biases.
Mutual Consistency Learning for Semi-supervised Medical Image Segmentation
In this paper, we propose a novel mutual consistency network (MC-Net+) to effectively exploit the unlabeled data for semi-supervised medical image segmentation.
Domain and Content Adaptive Convolution based Multi-Source Domain Generalization for Medical Image Segmentation
In the DAC module, a dynamic convolutional head is conditioned on the predicted domain code of the input to make our model adapt to the unseen target domain.
Boundary-aware Graph Reasoning for Semantic Segmentation
In this paper, we propose a Boundary-aware Graph Reasoning (BGR) module to learn long-range contextual features for semantic segmentation.
PSGR: Pixel-wise Sparse Graph Reasoning for COVID-19 Pneumonia Segmentation in CT Images
In the PSGR module, a graph is first constructed by projecting each pixel on a node based on the features produced by the segmentation backbone, and then converted into a sparsely-connected graph by keeping only K strongest connections to each uncertain pixel.
SMILE: Sparse-Attention based Multiple Instance Contrastive Learning for Glioma Sub-type Classification Using Pathological Image
In this paper, we propose a Sparse-attention based Multiple Instance contrastive LEarning (SMILE) method for glioma sub-type classification.
Learning Synergistic Attention for Light Field Salient Object Detection
We propose a novel Synergistic Attention Network (SA-Net) to address the light field salient object detection by establishing a synergistic effect between multi-modal features with advanced attention mechanisms.
Learning from Ambiguous Labels for Lung Nodule Malignancy Prediction
According to the consistency and reliability of their annotations, we divide nodules into three sets: a consistent and reliable set (CR-Set), an inconsistent set (IC-Set), and a low reliable set (LR-Set).
CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation
Convolutional neural networks (CNNs) have been the de facto standard for nowadays 3D medical image segmentation.
H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task
In this paper, we propose a Hybrid High-resolution and Non-local Feature Network (H2NF-Net) to segment brain tumor in multimodal MR images.
Deciding whether two quadratic surfaces actually intersect
We answer an open question proposed by P\'{o}lik and Terlaky in 2007 that: {\it how we can decide whether two quadratic surfaces intersect without actually computing the intersections?}
Optimization and Control 90C20, 90C22, 90C26 F.2
Solving a new type of quadratic optimization problem having a joint numerical range constraint
The objective function $F(f(x), g(x))$ is given as composition of a quadratic function $F(z)$ with two $n$-variate quadratic functions $z_1=f(x)$ and $z_2=g(x).$ In addition, it incorporates with a set of linear inequality constraints in $z=(z_1, z_2)^T,$ while having an implicit constraint that $z$ belongs to the joint numerical range of $(f, g).$ The formulation is very general in the sense that it covers quadratic programming with a single quadratic constraint of all types, including the inequality-type, the equality-type, and the interval-type.
Optimization and Control 90C20, 90C22, 90C26 F.2
Fully-Automated Liver Tumor Localization and Characterization from Multi-Phase MR Volumes Using Key-Slice ROI Parsing: A Physician-Inspired Approach
Using radiological scans to identify liver tumors is crucial for proper patient treatment.
Auto Learning Attention
Attention modules have been demonstrated effective in strengthening the representation ability of a neural network via reweighting spatial or channel features or stacking both operations sequentially.
Inter-layer Transition in Neural Architecture Search
Differential Neural Architecture Search (NAS) methods represent the network architecture as a repetitive proxy directed acyclic graph (DAG) and optimize the network weights and architecture weights alternatively in a differential manner.
Inter-slice Context Residual Learning for 3D Medical Image Segmentation
In this paper, we propose the 3D context residual network (ConResNet) for the accurate segmentation of 3D medical images.
PGL: Prior-Guided Local Self-supervised Learning for 3D Medical Image Segmentation
In this paper, we propose a PriorGuided Local (PGL) self-supervised model that learns the region-wise local consistency in the latent feature space.
DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets
To address this, we propose a dynamic on-demand network (DoDNet) that learns to segment multiple organs and tumors on partially labeled datasets.
Rethinking the Extraction and Interaction of Multi-Scale Features for Vessel Segmentation
Analyzing the morphological attributes of blood vessels plays a critical role in the computer-aided diagnosis of many cardiovascular and ophthalmologic diseases.
Pairwise Relation Learning for Semi-supervised Gland Segmentation
In this paper, we propose the pairwise relation-based semi-supervised (PRS^2) model for gland segmentation on histology images.
Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection
In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into an one-class classification-based anomaly detection problem, and thus propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module.
D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation
This function adds a weighted focal coefficient and combines two traditional loss functions.
A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification
Our results suggest that it is possible to boost the performance of skin lesion segmentation and classification simultaneously via training a unified model to perform both tasks in a mutual bootstrapping way.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Panoptic Segmentation with an End-to-End Cell R-CNN for Pathology Image Analysis
The morphological clues of various cancer cells are essential for pathologists to determine the stages of cancers.
Ranked #1 on
Nuclear Segmentation
on Cell17
Residual Network based Aggregation Model for Skin Lesion Classification
We recognize that the skin lesion diagnosis is an essential and challenging sub-task in Image classification, in which the Fisher vector (FV) encoding algorithm and deep convolutional neural network (DCNN) are two of the most successful techniques.
A Multi-Level Deep Ensemble Model for Skin Lesion Classification in Dermoscopy Images
A multi-level deep ensemble (MLDE) model that can be trained in an 'end to end' manner is proposed for skin lesion classification in dermoscopy images.
3D Global Convolutional Adversarial Network\\ for Prostate MR Volume Segmentation
In this paper, we propose a 3D Global Convolutional Adversarial Network (3D GCA-Net) to address efficient prostate MR volume segmentation.
ChestNet: A Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography
Computer-aided techniques may lead to more accurate and more acces-sible diagnosis of thorax diseases on chest radiography.
A Pulmonary Nodule Detection Model Based on Progressive Resolution and Hierarchical Saliency
Detection of pulmonary nodules on chest CT is an essential step in the early diagnosis of lung cancer, which is critical for best patient care.
Autonomous Deep Learning: A Genetic DCNN Designer for Image Classification
Recent years have witnessed the breakthrough success of deep convolutional neural networks (DCNNs) in image classification and other vision applications.
Detecting atrial fibrillation by deep convolutional neural networks
Conclusion The proposed method using deep convolutional neural networks shows high sensitivity, specificity and accuracy, and, therefore, is a valuable tool for AF detection.
Classification of Medical Images and Illustrations in the Biomedical Literature Using Synergic Deep Learning
The Classification of medical images and illustrations in the literature aims to label a medical image according to the modality it was produced or label an illustration according to its production attributes.
A Tribe Competition-Based Genetic Algorithm for Feature Selection in Pattern Classification
In this paper, a tribe competition-based genetic algorithm (TCbGA) is proposed for feature selection in pattern classification.
Similar Handwritten Chinese Character Discrimination by Weakly Supervised Learning
The unconstrained property makes our method well adapted to high variance in the size and position of discriminative regions in similar handwritten Chinese characters.
Robust Saliency Detection via Regularized Random Walks Ranking
In the field of saliency detection, many graph-based algorithms heavily depend on the accuracy of the pre-processed superpixel segmentation, which leads to significant sacrifice of detail information from the input image.
