Search Results for author:
Found 26 papers, 10 papers with code
STNet: Spatial and Temporal feature fusion network for change detection in remote sensing images
As an important task in remote sensing image analysis, remote sensing change detection (RSCD) aims to identify changes of interest in a region from spatially co-registered multi-temporal remote sensing images, so as to monitor the local development.
Improving Autoencoder-based Outlier Detection with Adjustable Probabilistic Reconstruction Error and Mean-shift Outlier Scoring
The MSS improved the performance of multiple autoencoder-based outlier detectors by an average of 20%.
Neighborhood Averaging for Improving Outlier Detectors
To verify our proposed hypothesis, we propose an outlier score post-processing technique for outlier detectors, called neighborhood averaging(NA), which pays attention to objects and their neighbors and guarantees them to have more similar outlier scores than their original scores.
FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularization
One is to regularize the frequency range of NeRF's inputs, while the other is to penalize the near-camera density fields.
Deep-learning-based on-chip rapid spectral imaging with high spatial resolution
Spectral imaging extends the concept of traditional color cameras to capture images across multiple spectral channels and has broad application prospects.
Human-machine Interactive Tissue Prototype Learning for Label-efficient Histopathology Image Segmentation
Then, the encoder is used to map the images into the embedding space and generate pixel-level pseudo tissue masks by querying the tissue prototype dictionary.
ConCL: Concept Contrastive Learning for Dense Prediction Pre-training in Pathology Images
We first benchmark representative SSL methods for dense prediction tasks in pathology images.
ReMix: A General and Efficient Framework for Multiple Instance Learning based Whole Slide Image Classification
We evaluate ReMix on two public datasets with two state-of-the-art MIL methods.
mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation
Concretely, we propose a novel multimodal Medical Transformer (mmFormer) for incomplete multimodal learning with three main components: the hybrid modality-specific encoders that bridge a convolutional encoder and an intra-modal Transformer for both local and global context modeling within each modality; an inter-modal Transformer to build and align the long-range correlations across modalities for modality-invariant features with global semantics corresponding to tumor region; a decoder that performs a progressive up-sampling and fusion with the modality-invariant features to generate robust segmentation.
Ranked #57 on
Semantic Segmentation
on NYU Depth v2
Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images
In this paper, we propose a Decoupled Pyramid Correlation Network (DPC-Net) that exploits attention mechanisms to fully leverage both low- and high-level features embedded in FCN to segment liver tumor.
Towards better understanding and better generalization of few-shot classification in histology images with contrastive learning
Histology images are a natural choice for such a study.
Towards Better Understanding and Better Generalization of Low-shot Classification in Histology Images with Contrastive Learning
Histology images are a natural choice for such study.
X2Teeth: 3D Teeth Reconstruction from a Single Panoramic Radiograph
Different from single object reconstruction from photos, this task has the unique challenge of constructing multiple objects at high resolutions.
Modality-aware Mutual Learning for Multi-modal Medical Image Segmentation
To this end, we propose a novel mutual learning (ML) strategy for effective and robust multi-modal liver tumor segmentation.
TumorCP: A Simple but Effective Object-Level Data Augmentation for Tumor Segmentation
Experiments on kidney tumor segmentation task demonstrate that TumorCP surpasses the strong baseline by a remarkable margin of 7. 12% on tumor Dice.
Atlas-aware ConvNetfor Accurate yet Robust Anatomical Segmentation
Second, we can largely boost the robustness of existing ConvNets, proved by: (i) testing on scans with synthetic pathologies, and (ii) training and evaluation on scans of different scanning setups across datasets.
Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer
Accurate segmentation of cardiac structures can assist doctors to diagnose diseases, and to improve treatment planning, which is highly demanded in the clinical practice.
Exploring Instance-Level Uncertainty for Medical Detection
The ability of deep learning to predict with uncertainty is recognized as key for its adoption in clinical routines.
Loss-analysis via Attention-scale for Physiologic Time Series
Physiologic signals have properties across multiple spatial and temporal scales, which can be shown by the complexity-analysis of the coarse-grained physiologic signals by scaling techniques such as the multiscale.
Simple Neighborhood Representative Pre-processing Boosts Outlier Detectors
Over the decades, traditional outlier detectors have ignored the group-level factor when calculating outlier scores for objects in data by evaluating only the object-level factor, failing to capture the collective outliers.
Oral-3D: Reconstructing the 3D Bone Structure of Oral Cavity from 2D Panoramic X-ray
In this paper, we propose a framework, named Oral-3D, to reconstruct the 3D oral cavity from a single PX image and prior information of the dental arch.
T-Net: Learning Feature Representation with Task-specific Supervision for Biomedical Image Analysis
The encoder-decoder network is widely used to learn deep feature representations from pixel-wise annotations in biomedical image analysis.
The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge
The 2019 Kidney and Kidney Tumor Segmentation challenge (KiTS19) was a competition held in conjunction with the 2019 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) which sought to address these issues and stimulate progress on this automatic segmentation problem.
Cascaded Volumetric Convolutional Network for Kidney Tumor Segmentation from CT volumes
Automated segmentation of kidney and tumor from 3D CT scans is necessary for the diagnosis, monitoring, and treatment planning of the disease.
A Generalised Seizure Prediction with Convolutional Neural Networks for Intracranial and Scalp Electroencephalogram Data Analysis
The proposed approach achieves sensitivity of 81. 4%, 81. 2%, 82. 3% and false prediction rate (FPR) of 0. 06/h, 0. 16/h, 0. 22/h on Freiburg Hospital intracranial EEG (iEEG) dataset, Children's Hospital of Boston-MIT scalp EEG (sEEG) dataset, and Kaggle American Epilepsy Society Seizure Prediction Challenge's dataset, respectively.
Supervised Learning in Automatic Channel Selection for Epileptic Seizure Detection
We present a novel method for automatic seizure detection based on iEEG data that outperforms current state-of-the-art seizure detection methods in terms of computational efficiency while maintaining the accuracy.
