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Article
A Computational Algorithm for Calculating Fracture Index of Core Runs
Fracture Index (FI), which represents the count of fractures over an arbitrary length of core with similar intensity of fracturing, provides insight into the fracture state of rock masses. Manual interpretatio...
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Chapter and Conference Paper
Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis
Cross-modality medical image synthesis is a critical topic and has the potential to facilitate numerous applications in the medical imaging field. Despite recent successes in deep-learning-based generative mod...
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Chapter and Conference Paper
Multi-scope Analysis Driven Hierarchical Graph Transformer for Whole Slide Image Based Cancer Survival Prediction
Cancer survival prediction requires considering not only the biological morphology but also the contextual interactions of tumor and surrounding tissues. The major limitation of previous learning frameworks fo...
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Chapter and Conference Paper
Cross-View Deformable Transformer for Non-displaced Hip Fracture Classification from Frontal-Lateral X-Ray Pair
Hip fractures are a common cause of morbidity and mortality and are usually diagnosed from the X-ray images in clinical routine. Deep learning has achieved promising progress for automatic hip fracture detecti...
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Chapter and Conference Paper
Consistency-Guided Meta-learning for Bootstrap** Semi-supervised Medical Image Segmentation
Medical imaging has witnessed remarkable progress but usually requires a large amount of high-quality annotated data which is time-consuming and costly to obtain. To alleviate this burden, semi-supervised lear...
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Chapter and Conference Paper
HIGT: Hierarchical Interaction Graph-Transformer for Whole Slide Image Analysis
In computation pathology, the pyramid structure of gigapixel Whole Slide Images (WSIs) has recently been studied for capturing various information from individual cell interactions to tissue microenvironments....
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Chapter and Conference Paper
MuST: Multimodal Spatiotemporal Graph-Transformer for Hospital Readmission Prediction
Hospital readmission prediction is considered an essential approach to decreasing readmission rates, which is a key factor in assessing the quality and efficacy of a healthcare system. Previous studies have ex...
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Article
Open AccessLeveraging data-driven self-consistency for high-fidelity gene expression recovery
Single cell RNA sequencing is a promising technique to determine the states of individual cells and classify novel cell subtypes. In current sequence data analysis, however, genes with low expressions are omit...
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Chapter and Conference Paper
Joint Prediction of Meningioma Grade and Brain Invasion via Task-Aware Contrastive Learning
Preoperative and noninvasive prediction of the meningioma grade is important in clinical practice, as it directly influences the clinical decision making. What’s more, brain invasion in meningioma (i.e., the pres...
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Chapter and Conference Paper
Spatial-Hierarchical Graph Neural Network with Dynamic Structure Learning for Histological Image Classification
Graph neural network (GNN) has achieved tremendous success in histological image classification, as it can explicitly model the notion and interaction of different biological entities (e.g., cell, tissue and etc.
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Chapter and Conference Paper
Reinforcement Learning Driven Intra-modal and Inter-modal Representation Learning for 3D Medical Image Classification
Multi-modality 3D medical images play an important role in the clinical practice. Due to the effectiveness of exploring the complementary information among different modalities, multi-modality learning has att...
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Chapter and Conference Paper
Multi-task Learning-Driven Volume and Slice Level Contrastive Learning for 3D Medical Image Classification
Automatic 3D medical image classification,e.g., brain tumor grading from 3D MRI images, is important in clinical practice. However, direct tumor grading from 3D MRI images is quite challenging due to the unknown ...
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Chapter and Conference Paper
NestedFormer: Nested Modality-Aware Transformer for Brain Tumor Segmentation
Multi-modal MR imaging is routinely used in clinical practice to diagnose and investigate brain tumors by providing rich complementary information. Previous multi-modal MRI segmentation methods usually perform...
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Chapter and Conference Paper
CateNorm: Categorical Normalization for Robust Medical Image Segmentation
Batch normalization (BN) uniformly shifts and scales the activations based on the statistics of a batch of images. However, the intensity distribution of the background pixels often dominates the BN statistics...
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Chapter and Conference Paper
You Should Look at All Objects
Feature pyramid network (FPN) is one of the key components for object detectors. However, there is a long-standing puzzle for researchers that the detection performance of large-scale objects are usually suppr...
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Chapter and Conference Paper
TransCT: Dual-Path Transformer for Low Dose Computed Tomography
Low dose computed tomography (LDCT) has attracted more and more attention in routine clinical diagnosis assessment, therapy planning, etc., which can reduce the dose of X-ray radiation to patients. However, the n...
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Chapter and Conference Paper
Selective Learning from External Data for CT Image Segmentation
Learning from external data is an effective and efficient way of training deep networks, which can substantially alleviate the burden on collecting training data and annotations. It is of great significance in...
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Chapter and Conference Paper
Difficulty-Aware Meta-learning for Rare Disease Diagnosis
Rare diseases have extremely low-data regimes, unlike common diseases with large amount of available labeled data. Hence, to train a neural network to classify rare diseases with a few per-class data samples i...
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Chapter and Conference Paper
Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-Efficient Cardiac Segmentation
Medical image annotations are prohibitively time-consuming and expensive to obtain. To alleviate annotation scarcity, many approaches have been developed to efficiently utilize extra information, e.g., semi-super...
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Chapter and Conference Paper
Local and Global Structure-Aware Entropy Regularized Mean Teacher Model for 3D Left Atrium Segmentation
Emerging self-ensembling methods have achieved promising semi-supervised segmentation performances on medical images through forcing consistent predictions of unannotated data under different perturbations. Ho...
