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Found 51 papers, 18 papers with code
Matroid and Knapsack Center Problems
We also consider the outlier version of the problem where a given number of vertices can be excluded as the outliers from the solution.
Data Structures and Algorithms Discrete Mathematics
Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation
Segmentation of 3D images is a fundamental problem in biomedical image analysis.
Optimizing Memory Efficiency for Convolution Kernels on Kepler GPUs
The high computation throughput and memory bandwidth of graphics processing units (GPUs) make GPUs a natural choice for accelerating convolution operations.
Neuron Segmentation Using Deep Complete Bipartite Networks
We propose a new FCN-type deep learning model, called deep complete bipartite networks (CB-Net), and a new scheme for leveraging approximate instance-wise annotation to train our pixel-wise prediction model.
Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
Image segmentation is a fundamental problem in biomedical image analysis.
A New Registration Approach for Dynamic Analysis of Calcium Signals in Organs
A key to analyzing spatial-temporal patterns of $Ca^{2+}$ signal waves is to accurately align the pouches across image sequences.
BoxNet: Deep Learning Based Biomedical Image Segmentation Using Boxes Only Annotation
In recent years, deep learning (DL) methods have become powerful tools for biomedical image segmentation.
Deep Learning Based Instance Segmentation in 3D Biomedical Images Using Weak Annotation
Our approach needs only 3D bounding boxes for all instances and full voxel annotation for a small fraction of the instances, and uses a novel two-stage 3D instance segmentation model utilizing these two kinds of annotation, respectively.
A New Ensemble Learning Framework for 3D Biomedical Image Segmentation
In this paper, we propose a new ensemble learning framework for 3D biomedical image segmentation that combines the merits of 2D and 3D models.
Ranked #1 on
Cardiovascular MR Segmentaiton
on HVSMR 2016
CC-Net: Image Complexity Guided Network Compression for Biomedical Image Segmentation
Convolutional neural networks (CNNs) for biomedical image analysis are often of very large size, resulting in high memory requirement and high latency of operations.
Cascade Decoder: A Universal Decoding Method for Biomedical Image Segmentation
The cascade decoder structure aims to conduct more effective decoding of hierarchically encoded features and is more compatible with common encoders than the known decoders.
SPDA: Superpixel-based Data Augmentation for Biomedical Image Segmentation
To better mimic human visual perception, we think it is desirable for the deep learning model to be able to perceive not only raw images but also SP images.
Decompose-and-Integrate Learning for Multi-class Segmentation in Medical Images
Ablation study confirms the effectiveness of our proposed learning scheme for medical images.
Unlabeled Data Guided Semi-supervised Histopathology Image Segmentation
Second, we devise an effective data selection policy for judiciously sampling the generated images: (1) to make the generated training set better cover the dataset, the clusters that are underrepresented in the original training set are covered more; (2) to make the training process more effective, we identify and oversample the images of "hard cases" in the data for which annotated training data may be scarce.
Flow-Mixup: Classifying Multi-labeled Medical Images with Corrupted Labels
In clinical practice, medical image interpretation often involves multi-labeled classification, since the affected parts of a patient tend to present multiple symptoms or comorbidities.
Doctor Imitator: Hand-Radiography-based Bone Age Assessment by Imitating Scoring Methods
The architecture of DI is designed to learn the diagnostic logistics of doctors using the scoring methods (e. g., the Tanner-Whitehouse method) for bone age assessment.
Objective-Dependent Uncertainty Driven Retinal Vessel Segmentation
In this paper, we study retinal vessel segmentation by incorporating tiny vessel segmentation into our framework for the overall accurate vessel segmentation.
Electrocardio Panorama: Synthesizing New ECG Views with Self-supervision
For the first time, we propose a new concept, Electrocardio Panorama, which allows visualizing ECG signals from any queried viewpoints.
Image Complexity Guided Network Compression for Biomedical Image Segmentation
Finally, the mapping is used to determine the convolutional layer-wise multiplicative factor for generating a compressed network.
Hierarchical Self-Supervised Learning for Medical Image Segmentation Based on Multi-Domain Data Aggregation
Unlike the current literature on task-specific self-supervised pretraining followed by supervised fine-tuning, we utilize SSL to learn task-agnostic knowledge from heterogeneous data for various medical image segmentation tasks.
DANets: Deep Abstract Networks for Tabular Data Classification and Regression
A special basic block is built using AbstLays, and we construct a family of Deep Abstract Networks (DANets) for tabular data classification and regression by stacking such blocks.
AGMI: Attention-Guided Multi-omics Integration for Drug Response Prediction with Graph Neural Networks
Accurate drug response prediction (DRP) is a crucial yet challenging task in precision medicine.
D-Former: A U-shaped Dilated Transformer for 3D Medical Image Segmentation
In this paper, we propose a new method called Dilated Transformer, which conducts self-attention for pair-wise patch relations captured alternately in local and global scopes.
Improving Human Sperm Head Morphology Classification with Unsupervised Anatomical Feature Distillation
With rising male infertility, sperm head morphology classification becomes critical for accurate and timely clinical diagnosis.
H-EMD: A Hierarchical Earth Mover's Distance Method for Instance Segmentation
We observe that probability maps by DL semantic segmentation models can be used to generate many possible instance candidates, and accurate instance segmentation can be achieved by selecting from them a set of "optimized" candidates as output instances.
Data-Driven Deep Supervision for Medical Image Segmentation
In this paper, we study medical image segmentation by focusing on robust data-specific feature extraction to achieve improved dense prediction.
Identifying Electrocardiogram Abnormalities Using a Handcrafted-Rule-Enhanced Neural Network
Automatic ECG classification methods, especially the deep learning based ones, have been proposed to detect cardiac abnormalities using ECG records, showing good potential to improve clinical diagnosis and help early prevention of cardiovascular diseases.
Usable Region Estimate for Assessing Practical Usability of Medical Image Segmentation Models
We aim to quantitatively measure the practical usability of medical image segmentation models: to what extent, how often, and on which samples a model's predictions can be used/trusted.
ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases
Electrocardiogram (ECG) is a widely used non-invasive diagnostic tool for heart diseases.
Data-Driven Deep Supervision for Skin Lesion Classification
Specifically, we analyze the convolutional network's behavior (field-of-view) to find the location of deep supervision for improved feature extraction.
Robust Training of Graph Neural Networks via Noise Governance
To address these issues, we propose a novel RTGNN (Robust Training of Graph Neural Networks via Noise Governance) framework that achieves better robustness by learning to explicitly govern label noise.
ConvFormer: Combining CNN and Transformer for Medical Image Segmentation
(2) A residual-shaped hybrid stem based on a combination of convolutions and Enhanced DeTrans is developed to capture both local and global representations to enhance representation ability.
Unsupervised Feature Clustering Improves Contrastive Representation Learning for Medical Image Segmentation
Self-supervised instance discrimination is an effective contrastive pretext task to learn feature representations and address limited medical image annotations.
A Point in the Right Direction: Vector Prediction for Spatially-aware Self-supervised Volumetric Representation Learning
High annotation costs and limited labels for dense 3D medical imaging tasks have recently motivated an assortment of 3D self-supervised pretraining methods that improve transfer learning performance.
Keep Your Friends Close & Enemies Farther: Debiasing Contrastive Learning with Spatial Priors in 3D Radiology Images
Understanding of spatial attributes is central to effective 3D radiology image analysis where crop-based learning is the de facto standard.
T2G-Former: Organizing Tabular Features into Relation Graphs Promotes Heterogeneous Feature Interaction
Recent development of deep neural networks (DNNs) for tabular learning has largely benefited from the capability of DNNs for automatic feature interaction.
GPT4MIA: Utilizing Generative Pre-trained Transformer (GPT-3) as A Plug-and-Play Transductive Model for Medical Image Analysis
In this paper, we propose a novel approach (called GPT4MIA) that utilizes Generative Pre-trained Transformer (GPT) as a plug-and-play transductive inference tool for medical image analysis (MIA).
Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model
Thus, how to utilize such a large foundation model for medical image segmentation is an emerging research target.
SwIPE: Efficient and Robust Medical Image Segmentation with Implicit Patch Embeddings
Modern medical image segmentation methods primarily use discrete representations in the form of rasterized masks to learn features and generate predictions.
SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation
Our new method is iterative and consists of two main stages: (1) segmentation model training; (2) expanding the labeled set by using the trained segmentation model, an unlabeled set, SAM, and domain-specific knowledge.
RR-CP: Reliable-Region-Based Conformal Prediction for Trustworthy Medical Image Classification
Conformal prediction (CP) generates a set of predictions for a given test sample such that the prediction set almost always contains the true label (e. g., 99. 5\% of the time).
Current and future directions in network biology
As such, it is expected to help shape short- and long-term vision for future computational and algorithmic research in network biology.
GCL: Gradient-Guided Contrastive Learning for Medical Image Segmentation with Multi-Perspective Meta Labels
Since annotating medical images for segmentation tasks commonly incurs expensive costs, it is highly desirable to design an annotation-efficient method to alleviate the annotation burden.
OneSeg: Self-learning and One-shot Learning based Single-slice Annotation for 3D Medical Image Segmentation
As deep learning methods continue to improve medical image segmentation performance, data annotation is still a big bottleneck due to the labor-intensive and time-consuming burden on medical experts, especially for 3D images.
PHG-Net: Persistent Homology Guided Medical Image Classification
The PH module is lightweight and capable of integrating topological features into any CNN or Transformer architectures in an end-to-end fashion.
U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation
We evaluate our method on several public medical image segmentation datasets for skin lesion segmentation and polyp segmentation, and the experimental results demonstrate the segmentation accuracy of our new method over state-of-the-art methods, while preserving memory and computational efficiency.
SQA-SAM: Segmentation Quality Assessment for Medical Images Utilizing the Segment Anything Model
Segmentation quality assessment (SQA) plays a critical role in the deployment of a medical image based AI system.
AI-Enhanced Virtual Reality in Medicine: A Comprehensive Survey
With the rapid advance of computer graphics and artificial intelligence technologies, the ways we interact with the world have undergone a transformative shift.
Densely Decoded Networks with Adaptive Deep Supervision for Medical Image Segmentation
Medical image segmentation using deep neural networks has been highly successful.
Making Pre-trained Language Models Great on Tabular Prediction
Condensing knowledge from diverse domains, language models (LMs) possess the capability to comprehend feature names from various tables, potentially serving as versatile learners in transferring knowledge across distinct tables and diverse prediction tasks, but their discrete text representation space is inherently incompatible with numerical feature values in tables.
Path-GPTOmic: A Balanced Multi-modal Learning Framework for Survival Outcome Prediction
For predicting cancer survival outcomes, standard approaches in clinical research are often based on two main modalities: pathology images for observing cell morphology features, and genomic (e. g., bulk RNA-seq) for quantifying gene expressions.
