Linhao Qu
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View article: ME-Mamba: Multi-Expert Mamba with Efficient Knowledge Capture and Fusion for Multimodal Survival Analysis
ME-Mamba: Multi-Expert Mamba with Efficient Knowledge Capture and Fusion for Multimodal Survival Analysis Open
Survival analysis using whole-slide images (WSIs) is crucial in cancer research. Despite significant successes, pathology images typically only provide slide-level labels, which hinders the learning of discriminative representations from g…
View article: An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution
An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution Open
High-quality whole-slide scanning is expensive, complex, and time-consuming, thus limiting the acquisition and utilization of high-resolution histopathology images in daily clinical work. Deep learning-based single-image super-resolution (…
View article: Foundation model-driven multimodal prognostic prediction in patients undergoing primary surgery for high-grade serous ovarian cancer
Foundation model-driven multimodal prognostic prediction in patients undergoing primary surgery for high-grade serous ovarian cancer Open
High-grade serous ovarian cancer (HGSOC) presents challenges in prognostic prediction. This study aimed to develop a universal foundation model-driven multimodal model (FoMu model) to assess the prognosis of HGSOC patients. We conducted a …
View article: Towards Unified Molecule-Enhanced Pathology Image Representation Learning via Integrating Spatial Transcriptomics
Towards Unified Molecule-Enhanced Pathology Image Representation Learning via Integrating Spatial Transcriptomics Open
Recent advancements in multimodal pre-training models have significantly advanced computational pathology. However, current approaches predominantly rely on visual-language models, which may impose limitations from a molecular perspective …
View article: Local Implicit Wavelet Transformer for Arbitrary-Scale Super-Resolution
Local Implicit Wavelet Transformer for Arbitrary-Scale Super-Resolution Open
Implicit neural representations have recently demonstrated promising potential in arbitrary-scale Super-Resolution (SR) of images. Most existing methods predict the pixel in the SR image based on the queried coordinate and ensemble nearby …
View article: FANCL: Feature-Guided Attention Network with Curriculum Learning for Brain Metastases Segmentation
FANCL: Feature-Guided Attention Network with Curriculum Learning for Brain Metastases Segmentation Open
Accurate segmentation of brain metastases (BMs) in MR image is crucial for the diagnosis and follow-up of patients. Methods based on deep convolutional neural networks (CNNs) have achieved high segmentation performance. However, due to the…
View article: FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classification
FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classification Open
The expensive fine-grained annotation and data scarcity have become the primary obstacles for the widespread adoption of deep learning-based Whole Slide Images (WSI) classification algorithms in clinical practice. Unlike few-shot learning …
View article: MSCPT: Few-shot Whole Slide Image Classification with Multi-scale and Context-focused Prompt Tuning
MSCPT: Few-shot Whole Slide Image Classification with Multi-scale and Context-focused Prompt Tuning Open
Multiple instance learning (MIL) has become a standard paradigm for the weakly supervised classification of whole slide images (WSIs). However, this paradigm relies on using a large number of labeled WSIs for training. The lack of training…
View article: Advancing H&E-to-IHC Stain Translation in Breast Cancer: A Multi-Magnification and Attention-Based Approach
Advancing H&E-to-IHC Stain Translation in Breast Cancer: A Multi-Magnification and Attention-Based Approach Open
Breast cancer presents a significant healthcare challenge globally, demanding precise diagnostics and effective treatment strategies, where histopathological examination of Hematoxylin and Eosin (H&E) stained tissue sections plays a centra…
View article: Multi-modal Data Binding for Survival Analysis Modeling with Incomplete Data and Annotations
Multi-modal Data Binding for Survival Analysis Modeling with Incomplete Data and Annotations Open
Survival analysis stands as a pivotal process in cancer treatment research, crucial for predicting patient survival rates accurately. Recent advancements in data collection techniques have paved the way for enhancing survival predictions b…
View article: Deep Mutual Learning among Partially Labeled Datasets for Multi-Organ Segmentation
Deep Mutual Learning among Partially Labeled Datasets for Multi-Organ Segmentation Open
The task of labeling multiple organs for segmentation is a complex and time-consuming process, resulting in a scarcity of comprehensively labeled multi-organ datasets while the emergence of numerous partially labeled datasets. Current meth…
View article: Pathology-knowledge Enhanced Multi-instance Prompt Learning for Few-shot Whole Slide Image Classification
Pathology-knowledge Enhanced Multi-instance Prompt Learning for Few-shot Whole Slide Image Classification Open
Current multi-instance learning algorithms for pathology image analysis often require a substantial number of Whole Slide Images for effective training but exhibit suboptimal performance in scenarios with limited learning data. In clinical…
View article: Asynchronous Multimodal Video Sequence Fusion via Learning Modality-Exclusive and -Agnostic Representations
Asynchronous Multimodal Video Sequence Fusion via Learning Modality-Exclusive and -Agnostic Representations Open
Understanding human intentions (e.g., emotions) from videos has received considerable attention recently. Video streams generally constitute a blend of temporal data stemming from distinct modalities, including natural language, facial exp…
View article: Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentation
Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentation Open
Accurate segmentation of multiple organs in the head, neck, chest, and abdomen from medical images is an essential step in computer-aided diagnosis, surgical navigation, and radiation therapy. In the past few years, with a data-driven feat…
View article: Preoperative Molecular Subtype Classification Prediction of Ovarian Cancer Based on Multi-Parametric Magnetic Resonance Imaging Multi-Sequence Feature Fusion Network
Preoperative Molecular Subtype Classification Prediction of Ovarian Cancer Based on Multi-Parametric Magnetic Resonance Imaging Multi-Sequence Feature Fusion Network Open
In the study of the deep learning classification of medical images, deep learning models are applied to analyze images, aiming to achieve the goals of assisting diagnosis and preoperative assessment. Currently, most research classifies and…
View article: MAMILNet: advancing precision oncology with multi-scale attentional multi-instance learning for whole slide image analysis
MAMILNet: advancing precision oncology with multi-scale attentional multi-instance learning for whole slide image analysis Open
Background Whole Slide Image (WSI) analysis, driven by deep learning algorithms, has the potential to revolutionize tumor detection, classification, and treatment response prediction. However, challenges persist, such as limited model gene…
View article: Separate and Conquer: Decoupling Co-occurrence via Decomposition and Representation for Weakly Supervised Semantic Segmentation
Separate and Conquer: Decoupling Co-occurrence via Decomposition and Representation for Weakly Supervised Semantic Segmentation Open
Weakly supervised semantic segmentation (WSSS) with image-level labels aims to achieve segmentation tasks without dense annotations. However, attributed to the frequent coupling of co-occurring objects and the limited supervision from imag…
View article: An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution
An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution Open
High-quality whole-slide scanning is expensive, complex, and time-consuming, thus limiting the acquisition and utilization of high-resolution histopathology images in daily clinical work. Deep learning-based single-image super-resolution (…
View article: Fusionmlp: A Mlp-Based Unified Image Fusion Framework
Fusionmlp: A Mlp-Based Unified Image Fusion Framework Open
View article: OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology Image Classification
OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology Image Classification Open
Active learning (AL) is an effective approach to select the most informative samples to label so as to reduce the annotation cost. Existing AL methods typically work under the closed-set assumption, i.e., all classes existing in the unlabe…
View article: Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Good Instance Classifier is All You Need
Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Good Instance Classifier is All You Need Open
Weakly supervised whole slide image classification is usually formulated as a multiple instance learning (MIL) problem, where each slide is treated as a bag, and the patches cut out of it are treated as instances. Existing methods either t…
View article: Reducing Domain Gap in Frequency and Spatial Domain for Cross-Modality Domain Adaptation on Medical Image Segmentation
Reducing Domain Gap in Frequency and Spatial Domain for Cross-Modality Domain Adaptation on Medical Image Segmentation Open
Unsupervised domain adaptation (UDA) aims to learn a model trained on source domain and performs well on unlabeled target domain. In medical image segmentation field, most existing UDA methods depend on adversarial learning to address the …
View article: The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classification
The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classification Open
This paper introduces the novel concept of few-shot weakly supervised learning for pathology Whole Slide Image (WSI) classification, denoted as FSWC. A solution is proposed based on prompt learning and the utilization of a large language m…
View article: Towards Arbitrary-scale Histopathology Image Super-resolution: An Efficient Dual-branch Framework based on Implicit Self-texture Enhancement
Towards Arbitrary-scale Histopathology Image Super-resolution: An Efficient Dual-branch Framework based on Implicit Self-texture Enhancement Open
Existing super-resolution models for pathology images can only work in fixed integer magnifications and have limited performance. Though implicit neural network-based methods have shown promising results in arbitrary-scale super-resolution…
View article: Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentation
Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentation Open
Accurate segmentation of multiple organs of the head, neck, chest, and abdomen from medical images is an essential step in computer-aided diagnosis, surgical navigation, and radiation therapy. In the past few years, with a data-driven feat…
View article: Fusionmlp: A Mlp-Based Unified Image Fusion Framework
Fusionmlp: A Mlp-Based Unified Image Fusion Framework Open
View article: Reducing Domain Gap in Frequency and Spatial domain for Cross-modality Domain Adaptation on Medical Image Segmentation
Reducing Domain Gap in Frequency and Spatial domain for Cross-modality Domain Adaptation on Medical Image Segmentation Open
Unsupervised domain adaptation (UDA) aims to learn a model trained on source domain and performs well on unlabeled target domain. In medical image segmentation field, most existing UDA methods depend on adversarial learning to address the …
View article: Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification
Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification Open
Computer-aided pathology diagnosis based on the classification of Whole Slide Image (WSI) plays an important role in clinical practice, and it is often formulated as a weakly-supervised Multiple Instance Learning (MIL) problem. Existing me…
View article: Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysis
Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysis Open
Histopathological images contain abundant phenotypic information and pathological patterns, which are the gold standards for disease diagnosis and essential for the prediction of patient prognosis and treatment outcome. In recent years, co…
View article: Prediction Model of Hemorrhage Transformation in Patient with Acute Ischemic Stroke Based on Multiparametric MRI Radiomics and Machine Learning
Prediction Model of Hemorrhage Transformation in Patient with Acute Ischemic Stroke Based on Multiparametric MRI Radiomics and Machine Learning Open
Intravenous thrombolysis is the most commonly used drug therapy for patients with acute ischemic stroke, which is often accompanied by complications of intracerebral hemorrhage transformation (HT). This study proposed to build a reliable m…