12 Result(s)
within
Lequan Yu
Machine Learning
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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
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
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
Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization
The generalization capability of neural networks across domains is crucial for real-world applications. We argue that a generalized object recognition system should well understand the relationships among diff...
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Chapter and Conference Paper
Predicting Fluid Intelligence from MRI Images with Encoder-Decoder Regularization
In this paper, we develop a 3D convolutional neural network to predict the fluid intelligence from T1-weighted MRI images by adding an encoder-decoder regularization. Considering that cerebellar volume is ofte...
