---
# System prepended metadata

title: 長庚醫院合作案
tags: [Algorithm, Project]

---

長庚醫院合作案
===
###### tags: `Algorithm` `Project`

## Inflammatory Cells

### 長庚醫師提供

>### Artificial intelligence for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging
 >https://www.sciencedirect.com/science/article/pii/S2352396421001298 
>> How to identified and counted the number of eosinophils (n1), number of lymphocytes (n2), number of neutrophils (n3), and number of plasma cells (n4) of each patch.
>> 資料目前沒有公開需要透過申請
>https://www.researchgate.net/publication/358830207_A_comparative_study_of_artificial_intelligence_nasal_polyp_classification_based_on_whole-slide_imaging_and_JESREC_diagnostic_criteria
>https://zhuanlan.zhihu.com/p/374741294

>### Expert-level diagnosis of nasal polyps using deep learning on whole-slide imaging
> https://www.researchgate.net/publication/337856617_Expert-level_Diagnosis_of_Nasal_Polyps_Using_Deep_Learning_on_Whole-slide_Imaging

### 慢性鼻竇炎
>### 慢性鼻竇炎之診斷與治療
>http://www.tma.org.tw/ftproot%5C2012%5C20120518_14_07_43.pdf

### 數位病理
>### histomicstk Color Deconvolution 
>https://zhuanlan.zhihu.com/p/346365319
>### histolab
>https://zhuanlan.zhihu.com/p/346725676

### WSI 相關論文
>### From Patches to Slides: How to Train Deep Learning Models on Gigapixel Images With Weak Supervision
> https://towardsdatascience.com/from-patches-to-slides-how-to-train-deep-learning-models-on-gigapixel-images-with-weak-supervision-d2cd2081cfd7

>### A Deep Learning Approach for Colonoscopy Pathology WSI Analysis:Accurate Segmentation and Classiﬁcation
> https://www.researchgate.net/publication/347257459_A_Deep_Learning_Approach_for_Colonoscopy_Pathology_WSI_Analysis_Accurate_Segmentation_and_Classification

##＃ White Blood Cell classification 相關論文
>### Neutrophils Identification by Deep Learning and Voronoi Diagram of Clusters
> https://link.springer.com/content/pdf/10.1007/978-3-319-24574-4_27.pdf

>### White blood cell subtype detection and classification
> https://arxiv.org/pdf/2108.04614.pdf
> [Complete Blood Count (CBC) Dataset](https://github.com/MahmudulAlam/Complete-Blood-Cell-Count-Dataset)
 
>### A deep transfer learning framework for the automated assessment of corneal inflammation on in vivo confocal microscopy images
> https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0252653

>### Deep CNNs for Peripheral Blood Cell Classification
> https://arxiv.org/abs/2110.09508
>> Can we apply model built from blood cell images to blood cell images?

### 競爭對手
> ### 雲象科技
> https://www.aetherai.com/publications


## Auto-Segmentation
> ### A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy
> https://www.thegreenjournal.com/article/S0167-8140(21)06217-4/fulltext
> ### Multimodal Brain Tumor Segmentation Challenge 2020: Data
> https://www.med.upenn.edu/cbica/brats2020/data.html
> ### Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution.
> https://arxiv.org/pdf/2011.01045.pdf
> ### Automatically Segmenting Brain Tumors with AI
> https://developer.nvidia.com/blog/automatically-segmenting-brain-tumors-with-ai/
> ### 3D MRI brain tumor segmentation using autoencoder regularization
> https://arxiv.org/pdf/1810.11654.pdf
> ### QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis ofRanking Metrics and Benchmarking Results
> https://arxiv.org/pdf/2112.10074.pdf
> ### Fully automatic brain tumor segmentation for 3D evaluation in augmented reality
> https://thejns.org/configurable/content/journals$002fneurosurg-focus$002f51$002f2$002farticle-pE14.xml?t%3Aac=journals%24002fneurosurg-focus%24002f51%24002f2%24002farticle-pE14.xml&tab_body=pdf-22208
> https://app.box.com/s/xqd4ocqpxfd2hme1bq8up51nf1lr4pla

> ### Auto-Segmentation for Radiation Oncology
>https://app.box.com/s/g8id3wds4uwghxzcy1a4i19ptxwlenfq

## Hydrocephalus
> ### Deep Learning Achieves Neuroradiologist-Level Performance in Detecting Hydrocephalus （MRI)
> https://www.biorxiv.org/content/10.1101/2021.01.19.427328v1.full.pdf
> ### A Novel deep learning approach for the automated diagnosis of normal pressure hydrocephalus（MRI)
> https://www.researchgate.net/publication/350339542_A_Novel_deep_learning_approach_for_the_automated_diagnosis_of_normal_pressure_hydrocephalus
> 
> ### Automated Ventricular System Segmentation in Paediatric Patients Treated for Hydrocephalus Using Deep Learning Methods (CT) [github](https://github.com/fast-radiology/hydrocephalus)
> https://www.researchgate.net/publication/334289379_Automated_Ventricular_System_Segmentation_in_Paediatric_Patients_Treated_for_Hydrocephalus_Using_Deep_Learning_Methods 
> **資料數僅10筆**
> 
> ### Automated Segmentation of CT Scans for Normal Pressure Hydrocephalus (CT) [github](https://github.com/UCSB-VRL/NPH_Prediction)
>https://arxiv.org/pdf/1901.09088.pdf
**資料需申請 http://headctstudy.qure.ai/dataset**
