Supervised learning is constrained by the availability of labeled data, which are especially expensive to acquire in the field of digital pathology. Making use of open-source data for pre-training or using domain adaptation can be a way to overcome this issue. However, pre-trained networks often fail to generalize to new test domains that are not distributed identically due to tissue stainings, types, and textures variations. Additionally, current domain adaptation methods mainly rely on fully-labeled source datasets. In this work, we propose Self-Rule to Multi-Adapt (SRMA), which takes advantage of self-supervised learning to perform domain adaptation, and removes the necessity of fully-labeled source datasets. SRMA can effectively transfer the discriminative knowledge obtained from a few labeled source domain's data to a new target domain without requiring additional tissue annotations. Our method harnesses both domains' structures by capturing visual similarity with intra-domain and cross-domain self-supervision. Moreover, we present a generalized formulation of our approach that allows the framework to learn from multiple source domains. We show that our proposed method outperforms baselines for domain adaptation of colorectal tissue type classification \new{in single and multi-source settings}, and further validate our approach on an in-house clinical cohort. The code and trained models are available open-source: https://github.com/christianabbet/SRA.
Korea University（高丽大学）；University of Warwick（华威大学）
The appearance of histopathology images depends on tissue type, staining and digitization procedure. These vary from source to source and are the potential causes for domain-shift problems. Owing to this problem, despite the great success of deep learning models in computational pathology, a model trained on a specific domain may still perform sub-optimally when we apply them to another domain. To overcome this, we propose a new augmentation called PatchShuffling and a novel self-supervised contrastive learning framework named IMPaSh for pre-training deep learning models. Using these, we obtai…
EPFL（洛桑联邦理工学院）；Bern（伯尔尼大学）；Lausanne University Hospital（洛桑大学医院）；Center of Biomedical Imaging（生物医学成像中心）
With the long-term rapid increase in incidences of colorectal cancer (CRC), there is an urgent clinical need to improve risk stratification. The conventional pathology report is usually limited to only a few histopathological features. However, most of the tumor microenvironments used to describe patterns of aggressive tumor behavior are ignored. In this work, we aim to learn histopathological patterns within cancerous tissue regions that can be used to improve prognostic stratification for colorectal cancer. To do so, we propose a self-supervised learning method that jointly learns a represen…
Indian Institute of Technology Jodhpur（印度理工学院焦特布尔分校）；Shri Guruji Gobind Singhji Institute of Engineering and Technology（什里古鲁吉·戈宾德·辛格吉工程技术学院）
Colorectal cancer is a leading cause of death worldwide. However, early diagnosis dramatically increases the chances of survival, for which it is crucial to identify the tumor in the body. Since its imaging uses high-resolution techniques, annotating the tumor is time-consuming and requires particular expertise. Lately, methods built upon Convolutional Neural Networks(CNNs) have proven to be at par, if not better in many biomedical segmentation tasks. For the task at hand, we propose another CNN-based approach, which uses atrous convolutions and residual connections besides the conventional fi…
Towards a Benchmark for Colorectal Cancer Segmentation in Endorectal Ultrasound Videos: Dataset and Model Development
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore（新加坡科技研究局高性能计算研究所）；Affiliated Hospital of North Sichuan Medical College（川北医学院附属医院）；Shenzhen Research Institute of Big Data（深圳大数据研究院）
SurGen: 1020 H&E-stained Whole Slide Images With Survival and Genetic Markers
School of Computer Science University of St Andrews（计算机科学学院圣安德鲁大学）；School of Medicine University of St Andrews（医学学院圣安德鲁大学）；Lothian Biorepository NHS Lothian（洛锡安生物库NHS洛锡安）
University of Central Florida（中佛罗里达大学）；Mayo Clinic（妙佑医疗国际（梅奥诊所））；University of Catania（卡塔尼亚大学）
Colorectal cancer, largely arising from precursor lesions called polyps, remains one of the leading causes of cancer-related death worldwide. Current clinical standards require the resection and histopathological analysis of polyps due to test accuracy and sensitivity of optical biopsy methods falling substantially below recommended levels. In this study, we design a novel capsule network architecture (D-Caps) to improve the viability of optical biopsy of colorectal polyps. Our proposed method introduces several technical novelties including a novel capsule architecture with a capsule-average …
Qatar University（卡塔尔大学）；Izmir University of Economics（伊兹密尔经济大学）；Tampere University of Technology（坦佩雷理工大学）
Computer-aided diagnosis (CAD) based on histopathological imaging has progressed rapidly in recent years with the rise of machine learning based methodologies. Traditional approaches consist of training a classification model using features extracted from the images, based on textures or morphological properties. Recently, deep-learning based methods have been applied directly to the raw (unprocessed) data. However, their usability is impacted by the paucity of annotated data in the biomedical sector. In order to leverage the learning capabilities of deep Convolutional Neural Nets (CNNs) withi…
Indira Gandhi National Open University（英迪拉·甘地国立开放大学）
Colorectal cancer is one of fatal cancer worldwide. Colonoscopy is the standard treatment for examination, localization, and removal of colorectal polyps. However, it has been shown that the miss-rate of colorectal polyps during colonoscopy is between 6 to 27%. The use of an automated, accurate, and real-time polyp segmentation during colonoscopy examinations can help the clinicians to eliminate missing lesions and prevent further progression of colorectal cancer. The ``Medico automatic polyp segmentation challenge'' provides an opportunity to study polyp segmentation and build a fast segmenta…
CRC-ICM: Colorectal Cancer Immune Cell Markers Pattern Dataset
Isfahan University of Medical Sciences（伊斯法罕医科大学）；Poursina Hakim Digestive Diseases Research Center, Isfahan University of Medical Sciences（伊斯法罕医科大学普尔西纳哈基姆消化系统疾病研究中心）；Islamic Azad University（伊斯兰阿扎德大学）；Shiraz University of Medical Sciences（设拉子医科大学）
Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carcinoma. Colorectal polyps characterization relies on the histological analysis of tissue samples to determine the polyps malignancy and dysplasia grade. Deep neural networks achieve outstanding accuracy in medical patterns recognition, however they require large sets of annotated training images. We introduce UniToPatho, an annotated dataset of 9536 hematoxylin and eosin (H&E) stained patches extracted from 292 whole-…
An interpretable machine learning system for colorectal cancer diagnosis from pathology slides
Institute for Systems and Computer Engineering, Technology and Science (INESC TEC)（系统与计算机工程、技术与科学研究所（INESC TEC））；Faculty of Engineering, University of Porto (FEUP)（波尔图大学工程学院）；IMP Diagnostics（IMP诊断公司）；Research Center of IPO Porto (CI-IPOP)（波尔图IPO癌症研究所研究中心（CI-IPOP））；Portuguese Oncology Institute of Porto (IPO Porto)（葡萄牙波尔图癌症研究所）；Porto Comprehensive Cancer Center (Porto.CCC)（波尔图综合癌症中心）；School of Medicine and Biomedical Sciences – University of Porto (ICBAS-UP)（波尔图大学医学与生物医学科学学院（ICBAS-UP））
University of Saskatchewan（萨斯喀彻温大学）；University of Stanford（斯坦福大学）
Colorectal cancer is the third most common cancer-related death after lung cancer and breast cancer worldwide. The risk of developing colorectal cancer could be reduced by early diagnosis of polyps during a colonoscopy. Computer-aided diagnosis systems have the potential to be applied for polyp screening and reduce the number of missing polyps. In this paper, we compare the performance of different deep learning architectures as feature extractors, i.e. ResNet, DenseNet, InceptionV3, InceptionResNetV2 and SE-ResNeXt in the encoder part of a U-Net architecture. We validated the performance of p…
University of Bergen（卑尔根大学）；SimulaMet（西穆拉梅特）；Nepal Medical College（尼泊尔医学院）；Kathmandu University（加德满都大学）；UiT The Arctic University of Norway（挪威北极大学）；University of Oslo（奥斯陆大学）；Oslo Metropolitan University（奥斯陆城市大学）
Polyps are the predecessors to colorectal cancer which is considered as one of the leading causes of cancer-related deaths worldwide. Colonoscopy is the standard procedure for the identification, localization, and removal of colorectal polyps. Due to variability in shape, size, and surrounding tissue similarity, colorectal polyps are often missed by the clinicians during colonoscopy. With the use of an automatic, accurate, and fast polyp segmentation method during the colonoscopy, many colorectal polyps can be easily detected and removed. The ``Medico automatic polyp segmentation challenge'' p…
SimulaMet（西穆拉大都会研究所）；UiT The Arctic University of Norway（挪威UiT北极大学）；Oslo Metropolitan University（奥斯陆城市大学）；Augere Medical AS（奥杰雷医疗公司）；Sahlgrenska University Hospital（萨尔格伦斯卡大学医院）；Bærum Hospital（贝鲁姆医院）
Colorectal cancer is the third most common cause of cancer worldwide. According to Global cancer statistics 2018, the incidence of colorectal cancer is increasing in both developing and developed countries. Early detection of colon anomalies such as polyps is important for cancer prevention, and automatic polyp segmentation can play a crucial role for this. Regardless of the recent advancement in early detection and treatment options, the estimated polyp miss rate is still around 20\%. Support via an automated computer-aided diagnosis system could be one of the potential solutions for the over…
Efficient Self-Supervised Barlow Twins from Limited Tissue Slide Cohorts for Colonic Pathology Diagnostics
University of Concordia（康考迪亚大学）；University of Montreal（蒙特利尔大学）；Sunnybrook Health Science Centre（桑尼布鲁克健康科学中心）；Kingston General Hospital（金斯顿总医院）；Mila–Quebec Artificial Intelligence Institute（米拉-魁北克人工智能研究所）
Radboud University Medical Center（拉德堡德大学医学中心）；Laboratorium Pathologie Oost Nederland（东荷兰病理学实验室）
The development of reliable imaging biomarkers for the analysis of colorectal cancer (CRC) in hematoxylin and eosin (H&E) stained histopathology images requires an accurate and reproducible classification of the main tissue components in the image. In this paper, we propose a system for CRC tissue classification based on convolutional networks (ConvNets). We investigate the importance of stain normalization in tissue classification of CRC tissue samples in H&E-stained images. Furthermore, we report the performance of ConvNets on a cohort of rectal cancer samples and on an independent publicly …
Microscopic examination of tissues or histopathology is one of the diagnostic procedures for detecting colorectal cancer. The pathologist involved in such an examination usually identifies tissue type based on texture analysis, especially focusing on tumour-stroma ratio. In this work, we automate the task of tissue classification within colorectal cancer histology samples using deep transfer learning. We use discriminative fine-tuning with one-cycle-policy and apply structure-preserving colour normalization to boost our results. We also provide visual explanations of the deep neural network's …
Attachment Anchors: A Novel Framework for Laparoscopic Grasping Point Prediction in Colorectal Surgery
Technical University of Munich, TUM School of Medicine and Health, TUM University Hospital rechts der Isar, Department of Surgery, Research Group MITI（慕尼黑技术大学，TUM医学与健康学院，TUM慕尼黑大学医院rechts der Isar，外科部，MITI研究组）；Technical University of Munich, TUM School of Medicine and Health, TUM University Hospital rechts der Isar, Chair for AI in Healthcare and Medicine Munich（慕尼黑技术大学，TUM医学与健康学院，TUM慕尼黑大学医院rechts der Isar，人工智能在医疗与健康中的chair）；Department of Computing, Imperial College London（伦敦帝国学院计算系）
Expanding the Medical Decathlon dataset: segmentation of colon and colorectal cancer from computed tomography images
Neural Network Technologies Center（神经网络技术中心）；Institute of Urology and Human Reproductive Health（泌尿学与人类生殖健康研究所）；N.E. Bauman Moscow State Technical University（鲍曼莫斯科国立技术大学）；I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University)（俄罗斯联邦卫生部I.M.谢切诺夫第一莫斯科国立医科大学（谢切诺夫大学））；Hospital of Faculty Surgery No. 2, UKB No. 4（第4大学临床医院第二外科教学医院）；University Clinical Hospital No. 2（第二大学临床医院）
本文通过为 Medical Decathlon 数据集增加经放射科医生验证的结直肠癌 CT 分割标记，并训练 UNet 模型进行 5 折交叉验证，以提升结直肠癌自动分割与早期检测能力。
School of Computer Science（计算机科学学院）；School of Medicine（医学院）；University of St Andrews（圣安德鲁斯大学）
Digital pathology (DP) is a new research area which falls under the broad umbrella of health informatics. Owing to its potential for major public health impact, in recent years DP has been attracting much research attention. Nevertheless, a wide breadth of significant conceptual and technical challenges remain, few of them greater than those encountered in the field of oncology. The automatic analysis of digital pathology slides of cancerous tissues is particularly problematic due to the inherent heterogeneity of the disease, extremely large images, amongst numerous others. In this paper we in…
University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；Xiangya Hospital of Central South University（中南大学湘雅医院）
Colorectal polyps are abnormal tissues growing on the intima of the colon or rectum with a high risk of developing into colorectal cancer, the third leading cause of cancer death worldwide. Early detection and removal of colon polyps via colonoscopy have proved to be an effective approach to prevent colorectal cancer. Recently, various CNN-based computer-aided systems have been developed to help physicians detect polyps. However, these systems do not perform well in real-world colonoscopy operations due to the significant difference between images in a real colonoscopy and those in the public …
University of Science and Technology of China（中国科学技术大学）；UNSW Sydney（悉尼新南威尔士大学）；Genmab Inc.（Genmab公司）
Several deep learning algorithms have been developed to predict survival of cancer patients using whole slide images (WSIs).However, identification of image phenotypes within the WSIs that are relevant to patient survival and disease progression is difficult for both clinicians, and deep learning algorithms. Most deep learning based Multiple Instance Learning (MIL) algorithms for survival prediction use either top instances (e.g., maxpooling) or top/bottom instances (e.g., MesoNet) to identify image phenotypes. In this study, we hypothesize that wholistic information of the distribution of the…
University of Oxford（牛津大学）；University Hospitals Coventry and Warwickshire（考文垂与沃里克郡大学医院）；University of Warwick（华威大学）；Hamad Medical Corporation（哈马德医疗公司）；International Association for Cancer Research（国际癌症研究协会）
Distant metastasis is the major cause of death in colorectal cancer (CRC). Patients at high risk of developing distant metastasis could benefit from appropriate adjuvant and follow-up treatments if stratified accurately at an early stage of the disease. Studies have increasingly recognized the role of diverse cellular components within the tumor microenvironment in the development and progression of CRC tumors. In this paper, we show that a new method of automated analysis of digitized images from colorectal cancer tissue slides can provide important estimates of distant metastasis-free surviv…
Helmholtz Munich – German Research Center for Environment and Health（亥姆霍兹慕尼黑——德国环境健康研究中心）；Technical University of Munich（慕尼黑工业大学）；Technical University Dresden（德累斯顿工业大学）；University of Leeds（利兹大学）；Maastricht University Medical Center+（马斯特里赫特大学医学中心+）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；Johannes Kepler University Hospital Linz（林茨约翰·开普勒大学医院）；Gemeinschaftspraxis Pathologie, Starnberg（施塔恩贝格病理联合诊所）
Background: Deep learning (DL) can extract predictive and prognostic biomarkers from routine pathology slides in colorectal cancer. For example, a DL test for the diagnosis of microsatellite instability (MSI) in CRC has been approved in 2022. Current approaches rely on convolutional neural networks (CNNs). Transformer networks are outperforming CNNs and are replacing them in many applications, but have not been used for biomarker prediction in cancer at a large scale. In addition, most DL approaches have been trained on small patient cohorts, which limits their clinical utility. Methods: In th…
Dealing with the application of grading colorectal cancer images, this work proposes a 3 step pipeline for prediction of cancer levels from a histopathology image. The overall model performs better compared to other state of the art methods on the colorectal cancer grading data set and shows excellent performance for the extended colorectal cancer grading set. The performance improvements can be attributed to two main factors: The feature selection and graph augmentation method described here are spatially aware, but overall pixel position independent. Further, the graph size in terms of nodes…
Colorectal cancer is a leading cause of cancer death for both men and women. For this reason, histopathological characterization of colorectal polyps is the major instrument for the pathologist in order to infer the actual risk for cancer and to guide further follow-up. Colorectal polyps diagnosis includes the evaluation of the polyp type, and more importantly, the grade of dysplasia. This latter evaluation represents a critical step for the clinical follow-up. The proposed deep learning-based classification pipeline is based on state-of-the-art convolutional neural network, trained using prop…
Colorectal cancer (CRC) is one of the most commonly diagnosed cancers and a leading cause of cancer deaths in the United States. Colorectal polyps that grow on the intima of the colon or rectum is an important precursor for CRC. Currently, the most common way for colorectal polyp detection and precancerous pathology is the colonoscopy. Therefore, accurate colorectal polyp segmentation during the colonoscopy procedure has great clinical significance in CRC early detection and prevention. In this paper, we propose a novel end-to-end deep learning framework for the colorectal polyp segmentation. …
University of Texas at Austin（德克萨斯大学奥斯汀分校）；MD Anderson Cancer Center（MD安德森癌症中心）
In this study, to address the current high earlydetection miss rate of colorectal cancer (CRC) polyps, we explore the potentials of utilizing transfer learning and machine learning (ML) classifiers to precisely and sensitively classify the type of CRC polyps. Instead of using the common colonoscopic images, we applied three different ML algorithms on the 3D textural image outputs of a unique vision-based surface tactile sensor (VS-TS). To collect realistic textural images of CRC polyps for training the utilized ML classifiers and evaluating their performance, we first designed and additively m…
Data imbalance remains one of the open challenges in the contemporary machine learning. It is especially prevalent in case of medical data, such as histopathological images. Traditional data-level approaches for dealing with data imbalance are ill-suited for image data: oversampling methods such as SMOTE and its derivatives lead to creation of unrealistic synthetic observations, whereas undersampling reduces the amount of available data, critical for successful training of convolutional neural networks. To alleviate the problems associated with over- and undersampling we propose a novel two-st…
Northeastern University（东北大学）；Cancer Hospital, China Medical University（中国医科大学肿瘤医院）；Liaoning Cancer Hospital and Institute（辽宁省肿瘤医院暨研究所）；University of New South Wales（新南威尔士大学）；Stevens Institute of Technology（史蒂文斯理工学院）；Shengjing Hospital, China Medical University（中国医科大学附属盛京医院）；Liaoning Shihua University（辽宁石油化工大学）；University of Luebeck（吕贝克大学）
Background and purpose: Colorectal cancer has become the third most common cancer worldwide, accounting for approximately 10% of cancer patients. Early detection of the disease is important for the treatment of colorectal cancer patients. Histopathological examination is the gold standard for screening colorectal cancer. However, the current lack of histopathological image datasets of colorectal cancer, especially enteroscope biopsies, hinders the accurate evaluation of computer-aided diagnosis techniques. Methods: A new publicly available Enteroscope Biopsy Histopathological H&E Image Dataset…
Domain-specific transfer learning in the automated scoring of tumor-stroma ratio from histopathological images of colorectal cancer
University of Jyväskylä（于韦斯屈莱大学）；Oulu University Hospital（奥卢大学医院）；University of Oulu（奥卢大学）；Tampere University（坦佩雷大学）；University of Turku（图尔库大学）；Turku University Hospital（图尔库大学医院）；Hospital Nova of Central Finland（中芬兰新星医院）
本文提出基于卷积神经网络的结直肠癌组织病理图像 TSR 自动估算方法，评估领域特定迁移学习，最高三类分类准确率达 96.1%，与病理学家评估相关性为 0.57。
Exploring the Interplay Between Colorectal Cancer Subtypes Genomic Variants and Cellular Morphology: A Deep-Learning Approach
Faculty of Biomedical Engineering, Technion - Israel Institute of Technology（以色列理工学院生物医学工程学院）；Faculty of Computer Science, Technion - Israel Institute of Technology（以色列理工学院计算机科学学院）；Faculty of Biotechnology and Food Engineering, Technion - Israel Institute of Technology（以色列理工学院生物技术与食品工程学院）；Lokey Center for Life Science and Engineering, Technion - Israel Institute of Technology（以色列理工学院洛凯生命科学与工程中心）
Indian Institute of Science Education and Research Kolkata（印度科学教育与研究学院加尔各答分校）；Indian Institute of Technology Kanpur（印度理工学院坎普尔分校）
In this paper, we report a hidden Markov model based multiclass classification of cervical cancer tissues. This model has been validated directly over time series generated by the medium refractive index fluctuations extracted from differential interference contrast images of healthy and different stages of cancer tissues. The method shows promising results for multiclass classification with higher accuracy.
University of Warwick（华威大学）；University Hospitals Coventry and Warwickshire（考文垂和沃里克大学医院）；The Alan Turing Institute（艾伦·图灵研究所）
Synthetic images can be used for the development and evaluation of deep learning algorithms in the context of limited availability of data. In the field of computational pathology, where histology images are large in size and visual context is crucial, synthesis of large high resolution images via generative modeling is a challenging task. This is due to memory and computational constraints hindering the generation of large images. To address this challenge, we propose a novel SAFRON (Stitching Across the FRONtiers) framework to construct realistic, large high resolution tissue image tiles fro…
Indian Institute of Science Education and Research (IISER) Kolkata（印度科学教育与研究所加尔各答分校）；Institute of Engineering & Management, Kolkata（工程与管理学院（加尔各答））；Indian Institute of Technology (IIT) Kanpur（印度理工学院坎普尔分校）；College of Engineering & Management, Kolaghat（工程与管理学院（科拉加特））
In this paper, we have carried out the detail studies of pre-cancer by wavelet coherency and multifractal based detrended fluctuation analysis (MFDFA) on differential interference contrast (DIC) images of stromal region among different grades of pre-cancer tissues. Discrete wavelet transform (DWT) through Daubechies basis has been performed for identifying fluctuations over polynomial trends for clear characterization and differentiation of tissues. Wavelet coherence plots are performed for identifying the level of correlation in time scale plane between normal and various grades of DIC sample…
University of Toronto（多伦多大学）；Sunnybrook Research Institute（桑尼布鲁克研究所）；Centre Hospitalier Universitaire de Lyon（里昂大学医院中心）
Colorectal cancer is one of the most common and lethal cancers and colorectal cancer liver metastases (CRLM) is the major cause of death in patients with colorectal cancer. Multifocality occurs frequently in CRLM, but is relatively unexplored in CRLM outcome prediction. Most existing clinical and imaging biomarkers do not take the imaging features of all multifocal lesions into account. In this paper, we present an end-to-end autoencoder-based multiple instance neural network (AMINN) for the prediction of survival outcomes in multifocal CRLM patients using radiomic features extracted from cont…
A Classification-Based Adaptive Segmentation Pipeline: Feasibility Study Using Polycystic Liver Disease and Metastases from Colorectal Cancer CT Images
Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records
Geisel School of Medicine at Dartmouth（达特茅斯盖泽尔医学院）；Dartmouth-Hitchcock Medical Center（达特茅斯-希区柯克医学中心）；New Hampshire Colonoscopy Registry（新罕布什尔结肠镜检查登记处）；White River Junction VA Medical Center（怀特河交界处退伍军人事务医疗中心）；Dartmouth College（达特茅斯学院）
Indira Gandhi National Open University（英迪拉·甘地国立开放大学）；Larkin Community Hospital Palm Springs Campus（拉金社区医院棕榈泉院区）；Long Island Jewish Valley Stream（长岛犹太谷溪医院）；Feinberg School of Medicine, Northwestern University（西北大学范伯格医学院）
Colorectal cancer (CRC) is one of the most common causes of cancer and cancer-related mortality worldwide. Performing colon cancer screening in a timely fashion is the key to early detection. Colonoscopy is the primary modality used to diagnose colon cancer. However, the miss rate of polyps, adenomas and advanced adenomas remains significantly high. Early detection of polyps at the precancerous stage can help reduce the mortality rate and the economic burden associated with colorectal cancer. Deep learning-based computer-aided diagnosis (CADx) system may help gastroenterologists to identify po…
Dalian University of Technology（大连理工大学）；Peng Cheng Laboratory（鹏城实验室）
More than 90\% of colorectal cancer is gradually transformed from colorectal polyps. In clinical practice, precise polyp segmentation provides important information in the early detection of colorectal cancer. Therefore, automatic polyp segmentation techniques are of great importance for both patients and doctors. Most existing methods are based on U-shape structure and use element-wise addition or concatenation to fuse different level features progressively in decoder. However, both the two operations easily generate plenty of redundant information, which will weaken the complementarity betwe…
Improving Performance in Colorectal Cancer Histology Decomposition using Deep and Ensemble Machine Learning
University of Jyväskylä（于韦斯屈莱大学）；University of Helsinki（赫尔辛基大学）；University of Turku（图尔库大学）；Turku University Hospital（图尔库大学医院）；Hospital Nova of Central Finland（中芬兰诺瓦医院）
Technische Hochschule Ingolstadt（英戈尔施塔特应用技术大学）；Klinikum Ingolstadt（英戈尔施塔特医院）；University of Regensburg（雷根斯堡大学）；University Hospital Regensburg（雷根斯堡大学医院）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）
Attention-based multiple instance learning (AMIL) algorithms have proven to be successful in utilizing gigapixel whole-slide images (WSIs) for a variety of different computational pathology tasks such as outcome prediction and cancer subtyping problems. We extended an AMIL approach to the task of survival prediction by utilizing the classical Cox partial likelihood as a loss function, converting the AMIL model into a nonlinear proportional hazards model. We applied the model to tissue microarray (TMA) slides of 330 lung cancer patients. The results show that AMIL approaches can handle very sma…
