Wei-Hsiang Yu
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View article: Ranking-aware adapter for text-driven image ordering with CLIP
Ranking-aware adapter for text-driven image ordering with CLIP Open
Recent advances in vision-language models (VLMs) have made significant progress in downstream tasks that require quantitative concepts such as facial age estimation and image quality assessment, enabling VLMs to explore applications like i…
View article: Deep Residual Learning-Based Classification with Identification of Incorrect Predictions and Quantification of Cellularity and Nuclear Morphological Features in Digital Pathological Images of Common Astrocytic Tumors
Deep Residual Learning-Based Classification with Identification of Incorrect Predictions and Quantification of Cellularity and Nuclear Morphological Features in Digital Pathological Images of Common Astrocytic Tumors Open
Interobserver variations in the pathology of common astrocytic tumors impact diagnosis and subsequent treatment decisions. This study leveraged a residual neural network-50 (ResNet-50) in digital pathological images of diffuse astrocytoma,…
View article: Deep Learning–Based Nuclear Morphometry Reveals an Independent Prognostic Factor in Mantle Cell Lymphoma
Deep Learning–Based Nuclear Morphometry Reveals an Independent Prognostic Factor in Mantle Cell Lymphoma Open
Blastoid/pleomorphic morphology is associated with short survival in mantle cell lymphoma (MCL), but its prognostic value is overridden by Ki-67 in multivariate analysis. Herein, a nuclear segmentation model was developed using deep learni…
View article: Machine Learning Based on Morphological Features Enables Classification of Primary Intestinal T-Cell Lymphomas
Machine Learning Based on Morphological Features Enables Classification of Primary Intestinal T-Cell Lymphomas Open
The aim of this study was to investigate the feasibility of using machine learning techniques based on morphological features in classifying two subtypes of primary intestinal T-cell lymphomas (PITLs) defined according to the WHO criteria:…
View article: Identification of nodal micrometastasis in colorectal cancer using deep learning on annotation-free whole-slide images
Identification of nodal micrometastasis in colorectal cancer using deep learning on annotation-free whole-slide images Open
Detection of nodal micrometastasis (tumor size: 0.2-2.0 mm) is challenging for pathologists due to the small size of metastatic foci. Since lymph nodes with micrometastasis are counted as positive nodes, detecting micrometastasis is crucia…
View article: An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning
An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning Open
View article: An Annotation-free Whole-slide Training Approach to Pathological Classification of Lung Cancer Types by Deep Learning, whole-slide-cnn
An Annotation-free Whole-slide Training Approach to Pathological Classification of Lung Cancer Types by Deep Learning, whole-slide-cnn Open
This repository provides scripts to reproduce the results in the paper "An Annotation-free Whole-slide Training Approach to Pathological Classification of Lung Cancer Types by Deep Learning".
View article: Predicting aggressive histopathological features in esophageal cancer with positron emission tomography using a deep convolutional neural network
Predicting aggressive histopathological features in esophageal cancer with positron emission tomography using a deep convolutional neural network Open
A 3D-CNN can be trained, using PET imaging datasets, to predict LNV/PNI in esophageal cancer with acceptable accuracy.
View article: An Annotation-free Whole-slide Training Approach to Pathological Classification of Lung Cancer Types by Deep Neural Network
An Annotation-free Whole-slide Training Approach to Pathological Classification of Lung Cancer Types by Deep Neural Network Open
Deep learning for digital pathology is hindered by the extremely high spatial resolution of whole slide images (WSIs). Most studies adopt patch-based methods which, however, require well annotated data for training. These are typically don…
View article: Successful Identification of Nasopharyngeal Carcinoma in Nasopharyngeal Biopsies Using Deep Learning
Successful Identification of Nasopharyngeal Carcinoma in Nasopharyngeal Biopsies Using Deep Learning Open
Pathologic diagnosis of nasopharyngeal carcinoma (NPC) can be challenging since most cases are nonkeratinizing carcinoma with little differentiation and many admixed lymphocytes. Our aim was to evaluate the possibility to identify NPC in n…
View article: A Multi-Organ Nucleus Segmentation Challenge
A Multi-Organ Nucleus Segmentation Challenge Open
Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to…
View article: Deep Convolutional Neural Network-Based Positron Emission Tomography Analysis Predicts Esophageal Cancer Outcome
Deep Convolutional Neural Network-Based Positron Emission Tomography Analysis Predicts Esophageal Cancer Outcome Open
In esophageal cancer, few prediction tools can be confidently used in current clinical practice. We developed a deep convolutional neural network (CNN) with 798 positron emission tomography (PET) scans of esophageal squamous cell carcinoma…