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Found 42 papers, 17 papers with code
Two Stream Active Query Suggestion for Active Learning in Connectomics
As a use case, we build an end-to-end active learning framework with our query suggestion method for 3D synapse detection and mitochondria segmentation in connectomics.
S$^3$-TTA: Scale-Style Selection for Test-Time Augmentation in Biomedical Image Segmentation
Deep-learning models have been successful in biomedical image segmentation.
Structure-Preserving Instance Segmentation via Skeleton-Aware Distance Transform
Objects with complex structures pose significant challenges to existing instance segmentation methods that rely on boundary or affinity maps, which are vulnerable to small errors around contacting pixels that cause noticeable connectivity change.
Efficient Anatomical Labeling of Pulmonary Tree Structures via Implicit Point-Graph Networks
Pulmonary diseases rank prominently among the principal causes of death worldwide.
CLIPTrans: Transferring Visual Knowledge with Pre-trained Models for Multimodal Machine Translation
Simultaneously, there has been an influx of multilingual pre-trained models for NMT and multimodal pre-trained models for vision-language tasks, primarily in English, which have shown exceptional generalisation ability.
Domain-Scalable Unpaired Image Translation via Latent Space Anchoring
However, given a UNIT model trained on certain domains, it is difficult for current methods to incorporate new domains because they often need to train the full model on both existing and new domains.
The Impact of ChatGPT and LLMs on Medical Imaging Stakeholders: Perspectives and Use Cases
This study investigates the transformative potential of Large Language Models (LLMs), such as OpenAI ChatGPT, in medical imaging.
Calibrating Cross-modal Features for Text-Based Person Searching
On the other hand, the Masking Caption Modeling (MCM) loss leverages a masked captions prediction task to establish detailed and generic relationships between textual and visual parts.
Why is the winner the best?
The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.
The XPRESS Challenge: Xray Projectomic Reconstruction -- Extracting Segmentation with Skeletons
In this task, we provide volumetric XNH images of cortical white matter axons from the mouse brain along with ground truth annotations for axon trajectories.
An Out-of-Domain Synapse Detection Challenge for Microwasp Brain Connectomes
The size of image stacks in connectomics studies now reaches the terabyte and often petabyte scales with a great diversity of appearance across brain regions and samples.
QuantArt: Quantizing Image Style Transfer Towards High Visual Fidelity
The mechanism of existing style transfer algorithms is by minimizing a hybrid loss function to push the generated image toward high similarities in both content and style.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction
Based on the RibSeg v2, we develop a pipeline including deep learning-based methods for rib labeling, and a skeletonization-based method for centerline extraction.
Instance Segmentation of Unlabeled Modalities via Cyclic Segmentation GAN
Instance segmentation for unlabeled imaging modalities is a challenging but essential task as collecting expert annotation can be expensive and time-consuming.
Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training
Additionally, for better noise fitting, we present an efficient architecture Simple Multi-scale Network (SMNet) as the generator.
Ranked #1 on
Noise Estimation
on SIDD
Revisiting RCAN: Improved Training for Image Super-Resolution
Image super-resolution (SR) is a fast-moving field with novel architectures attracting the spotlight.
Texture-Based Error Analysis for Image Super-Resolution
Our key contribution is to leverage a texture classifier, which enables us to assign patches with semantic labels, to identify the source of SR errors both globally and locally.
YouMVOS: An Actor-Centric Multi-Shot Video Object Segmentation Dataset
Many video understanding tasks require analyzing multi-shot videos, but existing datasets for video object segmentation (VOS) only consider single-shot videos.
PyTorch Connectomics: A Scalable and Flexible Segmentation Framework for EM Connectomics
We present PyTorch Connectomics (PyTC), an open-source deep-learning framework for the semantic and instance segmentation of volumetric microscopy images, built upon PyTorch.
Object Propagation via Inter-Frame Attentions for Temporally Stable Video Instance Segmentation
Motivated by this, we propose a video instance segmentation method that alleviates the problem due to missing detections.
Ranked #33 on
Video Instance Segmentation
on YouTube-VIS validation
MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification
We introduce MedMNIST v2, a large-scale MNIST-like dataset collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D.
Intervention-based Recurrent Casual Model for Non-stationary Video Causal Discovery
Non-stationary casual structures are prevalent in real-world physical systems.
RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scans
Manual rib inspections in computed tomography (CT) scans are clinically critical but labor-intensive, as 24 ribs are typically elongated and oblique in 3D volumes.
Developmental Stage Classification of EmbryosUsing Two-Stream Neural Network with Linear-Chain Conditional Random Field
In this work, we propose a two-stream model for developmental stage classification.
NucMM Dataset: 3D Neuronal Nuclei Instance Segmentation at Sub-Cubic Millimeter Scale
Segmenting 3D cell nuclei from microscopy image volumes is critical for biological and clinical analysis, enabling the study of cellular expression patterns and cell lineages.
AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions
Electron microscopy (EM) enables the reconstruction of neural circuits at the level of individual synapses, which has been transformative for scientific discoveries.
Consistent Recurrent Neural Networks for 3D Neuron Segmentation
We present a recurrent network for the 3D reconstruction of neurons that sequentially generates binary masks for every object in an image with spatio-temporal consistency.
Context Reasoning Attention Network for Image Super-Resolution
However, the basic convolutional layer in CNNs is designed to extract local patterns, lacking the ability to model global context.
Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution
Specifically, we propose a dynamic high-pass filtering (HPF) module that locally applies adaptive filter weights for each spatial location and channel group to preserve high-frequency signals.
Learning Vector Quantized Shape Code for Amodal Blastomere Instance Segmentation
To alleviate the problem, we propose to classify input features into intermediate shape codes and recover complete object shapes from them.
MitoEM Dataset: Large-scale 3D Mitochondria Instance Segmentation from EM Images
On MitoEM, we find existing instance segmentation methods often fail to correctly segment mitochondria with complex shapes or close contacts with other instances.
Ranked #2 on
3D Instance Segmentation
on MitoEM
(AP75-R-Test metric)
Super-BPD: Super Boundary-to-Pixel Direction for Fast Image Segmentation
In this paper, we propose a fast image segmentation method based on a novel super boundary-to-pixel direction (super-BPD) and a customized segmentation algorithm with super-BPD.
Automated Measurements of Key Morphological Features of Human Embryos for IVF
A major challenge in clinical In-Vitro Fertilization (IVF) is selecting the highest quality embryo to transfer to the patient in the hopes of achieving a pregnancy.
A Topological Nomenclature for 3D Shape Analysis in Connectomics
Next, we develop nomenclature rules for pyramidal neurons and mitochondria from the reduced graph and finally learn the feature embedding for shape manipulation.
Parallel Separable 3D Convolution for Video and Volumetric Data Understanding
For video and volumetric data understanding, 3D convolution layers are widely used in deep learning, however, at the cost of increasing computation and training time.
Detecting Synapse Location and Connectivity by Signed Proximity Estimation and Pruning with Deep Nets
The few methods that computes direction along with contact location have only been demonstrated to work on either dyadic (most common in vertebrate brain) or polyadic (found in fruit fly brain) synapses, but not on both types.
Learning and Using the Arrow of Time
We seek to understand the arrow of time in videos -- what makes videos look like they are playing forwards or backwards?
Ranked #49 on
Self-Supervised Action Recognition
on UCF101
Self-Supervised Action Recognition
Temporal Action Localization
+1
Video Enhancement with Task-Oriented Flow
Many video enhancement algorithms rely on optical flow to register frames in a video sequence.
Ranked #7 on
Video Frame Interpolation
on Middlebury
Understanding Intra-Class Knowledge Inside CNN
Convolutional Neural Network (CNN) has been successful in image recognition tasks, and recent works shed lights on how CNN separates different classes with the learned inter-class knowledge through visualization.
Seeing the Arrow of Time
We explore whether we can observe Time's Arrow in a temporal sequence--is it possible to tell whether a video is running forwards or backwards?
A Video Representation Using Temporal Superpixels
We develop a generative probabilistic model for temporally consistent superpixels in video sequences.
