Rapid diagnosis of gastric cancer is a great challenge for clinical doctors. Dramatic progress of computer vision on gastric cancer has been made recently and this review focuses on advances during the past five years. Different methods for data generation and augmentation are presented, and various approaches to extract discriminative features compared and evaluated. Classification and segmentation techniques are carefully discussed for assisting more precise diagnosis and timely treatment. For classification, various methods have been developed to better proceed specific images, such as images with rotation and estimated real-timely (endoscopy), high resolution images (histopathology), low diagnostic accuracy images (X-ray), poor contrast images of the soft-tissue with cavity (CT) or those images with insufficient annotation. For detection and segmentation, traditional methods and machine learning methods are compared. Application of those methods will greatly reduce the labor and time consumption for the diagnosis of gastric cancers.
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（辽宁省肿瘤医院和研究所）；Heilongjiang University（黑龙江大学）；Stevens Institute of Technology（斯蒂文斯理工学院）；Shengjing Hospital, China Medical University（中国医科大学盛京医院）；University of Luebeck（吕贝克大学）
Background and Objective: Gastric cancer has turned out to be the fifth most common cancer globally, and early detection of gastric cancer is essential to save lives. Histopathological examination of gastric cancer is the gold standard for the diagnosis of gastric cancer. However, computer-aided diagnostic techniques are challenging to evaluate due to the scarcity of publicly available gastric histopathology image datasets. Methods: In this paper, a noble publicly available Gastric Histopathology Sub-size Image Database (GasHisSDB) is published to identify classifiers' performance. Specificall…
Cluster-Induced Mask Transformers for Effective Opportunistic Gastric Cancer Screening on Non-contrast CT Scans
DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Peking University（北京大学）；Hupan Lab（湖畔实验室）；Changsha Institute for Computing and Digital Economy（长沙计算与数字经济研究院）；Guangdong Province People’s Hospital（广东省人民医院）；The First Affiliated Hospital of Zhejiang University（浙江大学医学院附属第一医院）
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）；Shengjing Hospital, China Medical University（中国医科大学附属盛京医院）；University of Luebeck（吕贝克大学）
Gastric cancer is the fifth most common cancer in the world. At the same time, it is also the fourth most deadly cancer. Early detection of cancer exists as a guide for the treatment of gastric cancer. Nowadays, computer technology has advanced rapidly to assist physicians in the diagnosis of pathological pictures of gastric cancer. Ensemble learning is a way to improve the accuracy of algorithms, and finding multiple learning models with complementarity types is the basis of ensemble learning. The complementarity of sub-size pathology image classifiers when machine performance is insufficient…
Background and Objective: Non-invasively predicting the risk of cancer metastasis before surgery plays an essential role in determining optimal treatment methods for cancer patients (including who can benefit from neoadjuvant chemotherapy). Although developing radiomics based machine learning (ML) models has attracted broad research interest for this purpose, it often faces a challenge of how to build a highly performed and robust ML model using small and imbalanced image datasets. Methods: In this study, we explore a new approach to build an optimal ML model. A retrospective dataset involving…
The Chinese University of Hong Kong（香港中文大学）；the Sixth Affiliated Hospital of Sun Yat-sen University（中山大学附属第六医院）；Imsight Medical Technology Co., Ltd.（艾迈医疗科技有限公司）
The whole slide histopathology images (WSIs) play a critical role in gastric cancer diagnosis. However, due to the large scale of WSIs and various sizes of the abnormal area, how to select informative regions and analyze them are quite challenging during the automatic diagnosis process. The multi-instance learning based on the most discriminative instances can be of great benefit for whole slide gastric image diagnosis. In this paper, we design a recalibrated multi-instance deep learning method (RMDL) to address this challenging problem. We first select the discriminative instances, and then u…
In recent years, deep learning has successfully been applied to automate a wide variety of tasks in diagnostic histopathology. However, fast and reliable localization of small-scale regions-of-interest (ROI) has remained a key challenge, as discriminative morphologic features often occupy only a small fraction of a gigapixel-scale whole-slide image (WSI). In this paper, we propose a sparse WSI analysis method for the rapid identification of high-power ROI for WSI-level classification. We develop an evaluation framework inspired by the early classification literature, in order to quantify the t…
An Integrated AI-Enabled System Using One Class Twin Cross Learning (OCT-X) for Early Gastric Cancer Detection
Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）；Department of Biomedical Engineering, The Chinese University of Hong Kong（香港中文大学生物医学工程系）；Department of Neurology, David Geffen School of Medicine, University of California（加州大学洛杉矶分校神经医学系）；Guangdong Institute of Intelligence Science and Technology（广东智能科学与技术研究院）；Center for Brain-Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University（清华大学脑启发计算研究中心）；Department of Gastroenterology, Affiliated Hospital of Hebei University of Engineering（河北工程大学附属医院消化内科）；Institute of Artificial Intelligence, Chongqing Technology and Business University（重庆理工大学人工智能学院）；Cancer Centre, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau（澳门大学医学翻译中心癌症中心）；MoE Frontiers Science Center for Precision Oncology, University of Macau（澳门教育暨青年事务局精准肿瘤学前沿科学中心）；The director of the Institute of Clinical Medicine, The First People’s Hospital of Foshan（佛山第一人民医院临床医学研究所主任）；Hebei Key Laboratory of Medical Data Science, Institute of Biomedical Informatics, School of Medicine, Hebei University of Engineering（河北工程大学医学数据科学重点实验室）；The Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University（清华大学北京信息科学与技术国家研究中心）
HWA-UNETR: Hierarchical Window Aggregate UNETR for 3D Multimodal Gastric Lesion Segmentation
School of Computer Science and Engineering（计算机科学与工程学院）；School of Future Technology（未来技术学院）；South China University of Technology（华南理工大学）；Department of Medical Imaging（医学影像科）；Guangzhou Institute of Cancer Research（广州癌症研究所）；the Affiliated Cancer Hospital（附属肿瘤医院）；Guangzhou Medical University（广州医学院）；Department of Radiology（放射科）；Meizhou People’s Hospital（梅州市人民医院）；School of Computer Science and Technology（计算机科学与技术学院）；Hainan University（海南大学）；Department of Oncology Institute（肿瘤研究所）
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Guangzhou First People’s Hospital, School of Medicine, South China University of Technology（华南理工大学医学院附属广州市第一人民医院）；Guizhou Provincial People’s Hospital（贵州省人民医院）；Henan Cancer Hospital（河南省肿瘤医院）；Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences（广东省人民医院、广东省医学科学院）；Beihang University（北京航空航天大学）
Objective: Radiomics, an emerging tool for medical image analysis, is potential towards precisely characterizing gastric cancer (GC). Whether using one-slice 2D annotation or whole-volume 3D annotation remains a long-time debate, especially for heterogeneous GC. We comprehensively compared 2D and 3D radiomic features' representation and discrimination capacity regarding GC, via three tasks. Methods: Four-center 539 GC patients were retrospectively enrolled and divided into the training and validation cohorts. From 2D or 3D regions of interest (ROIs) annotated by radiologists, radiomic features…
Enhancing Diagnostic Precision in Gastric Bleeding through Automated Lesion Segmentation: A Deep DuS-KFCM Approach
University of Macau（澳门大学）；Guangdong Institute of Intelligence Science and Technology（广东省智能科学与技术研究院）；The Chinese University of Hong Kong（香港中文大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Chongqing Technology and Business University（重庆工商大学）；Institute of Artificial Intelligence, Chongqing Technology and Business University（重庆工商大学人工智能研究院）；Hebei University of Engineering（河北工程大学）
Northeastern University（东北大学）；Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；Stevens Institute of Technology（史蒂文斯理工学院）；China Medical University（中国医科大学）；Liaoning Cancer Hospital and Institute（辽宁省肿瘤医院暨研究所）；Chengdu University of Information Technology（成都信息工程大学）
In the Gastric Histopathology Image Classification (GHIC) tasks, which are usually weakly supervised learning missions, there is inevitably redundant information in the images. Therefore, designing networks that can focus on effective distinguishing features has become a popular research topic. In this paper, to accomplish the tasks of GHIC superiorly and to assist pathologists in clinical diagnosis, an intelligent Hierarchical Conditional Random Field based Attention Mechanism (HCRF-AM) model is proposed. The HCRF-AM model consists of an Attention Mechanism (AM) module and an Image Classifica…
Shanghai General Hospital（上海市第一人民医院）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）；SenseTime Research（商汤科技研究院）
Gastric cancer is one of the most common cancers, which ranks third among the leading causes of cancer death. Biopsy of gastric mucosa is a standard procedure in gastric cancer screening test. However, manual pathological inspection is labor-intensive and time-consuming. Besides, it is challenging for an automated algorithm to locate the small lesion regions in the gigapixel whole-slide image and make the decision correctly.To tackle these issues, we collected large-scale whole-slide image dataset with detailed lesion region annotation and designed a whole-slide image analyzing framework consi…
CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans
DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Johns Hopkins University（约翰斯·霍普金斯大学）；Hupan Lab（湖畔实验室）；Sun Yat-sen University Cancer Center（中山大学肿瘤防治中心）；Sichuan Cancer Hospital（四川省肿瘤医院）；The First Affiliated Hospital of Zhejiang University（浙江大学医学院附属第一医院）；Shengjing Hospital of China Medical University（中国医科大学附属盛京医院）；Guangdong Provincial People’s Hospital（广东省人民医院）
Multi-Scale Hybrid Vision Transformer for Learning Gastric Histology: AI-Based Decision Support System for Gastric Cancer Treatment
KAIST（韩国科学技术院）；Chungnam National University School of Medicine（忠南大学医学院）；Chungnam National University Hospital（忠南大学医院）；Chungnam National University Sejong Hospital（忠南大学世宗医院）
本文提出基于两阶段混合 ViT 与多尺度自注意力的 AI 系统，实现胃癌病理五分类并匹配治疗指南，在多中心切片中验证可提升灵敏度并缩短筛查时间。
Magnetic Resonance Imaging (MRI) is widely used for screening and staging prostate cancer. However, many prostate cancers have subtle features which are not easily identifiable on MRI, resulting in missed diagnoses and alarming variability in radiologist interpretation. Machine learning models have been developed in an effort to improve cancer identification, but current models localize cancer using MRI-derived features, while failing to consider the disease pathology characteristics observed on resected tissue. In this paper, we propose CorrSigNet, an automated two-step model that localizes p…
University of Connecticut（康涅狄格大学）；University of Connecticut Health Center（康涅狄格大学健康中心）
Prostate cancer is the most common cancer in men worldwide and the second leading cause of cancer death in the United States. One of the prognostic features in prostate cancer is the Gleason grading of histopathology images. The Gleason grade is assigned based on tumor architecture on Hematoxylin and Eosin (H&E) stained whole slide images (WSI) by the pathologists. This process is time-consuming and has known interobserver variability. In the past few years, deep learning algorithms have been used to analyze histopathology images, delivering promising results for grading prostate cancer. Howev…
Beijing University of Posts and Telecommunications（北京邮电大学）；Center for Research on Intelligent Perception and Computing, NLPR, CASIA（中国科学院自动化研究所模式识别国家重点实验室）；Shandong University of Science and Technology（山东科技大学）；Artificial Intelligence Research, CAS (CAS-AIR)（中国科学院人工智能研究院）
In recent years, deep convolutional neural networks have made significant advances in pathology image segmentation. However, pathology image segmentation encounters with a dilemma in which the higher-performance networks generally require more computational resources and storage. This phenomenon limits the employment of high-accuracy networks in real scenes due to the inherent high-resolution of pathological images. To tackle this problem, we propose CoCo DistillNet, a novel Cross-layer Correlation (CoCo) knowledge distillation network for pathological gastric cancer segmentation. Knowledge di…
iMD4GC: Incomplete Multimodal Data Integration to Advance Precise Treatment Response Prediction and Survival Analysis for Gastric Cancer
The Hong Kong University of Science and Technology（香港科技大学）；The Sixth Affiliated Hospital of Sun Yat-sen University（中山大学附属第六医院）；The First Affiliated Hospital of Kunming Medical University（昆明医科大学第一附属医院）；Sichuan Province Cancer Hospital, University of Electronic Science and Technology of China（电子科技大学四川省肿瘤医院）；The Chinese University of Hong Kong（香港中文大学）
Research Computing Center at the University of Chicago（芝加哥大学研究计算中心）
Prostate cancer is one of the most common causes of cancer deaths in men. There is a growing demand for noninvasively and accurately diagnostic methods that facilitate the current standard prostate cancer risk assessment in clinical practice. Still, developing computer-aided classification tools in prostate cancer diagnostics from multiparametric magnetic resonance images continues to be a challenge. In this work, we propose a novel deep learning approach for automatic classification of prostate lesions in the corresponding magnetic resonance images by constructing a two-stage multimodal multi…
Northeastern University（东北大学）；China Medical University（中国医科大学）；University of Luebeck（吕贝克大学）
The gold standard for gastric cancer detection is gastric histopathological image analysis, but there are certain drawbacks in the existing histopathological detection and diagnosis. In this paper, based on the study of computer aided diagnosis system, graph based features are applied to gastric cancer histopathology microscopic image analysis, and a classifier is used to classify gastric cancer cells from benign cells. Firstly, image segmentation is performed, and after finding the region, cell nuclei are extracted using the k-means method, the minimum spanning tree (MST) is drawn, and graph …
Gastric diffuse-type adenocarcinoma represents a disproportionately high percentage of cases of gastric cancers occurring in the young, and its relative incidence seems to be on the rise. Usually it affects the body of the stomach, and presents shorter duration and worse prognosis compared with the differentiated (intestinal) type adenocarcinoma. The main difficulty encountered in the differential diagnosis of gastric adenocarcinomas occurs with the diffuse-type. As the cancer cells of diffuse-type adenocarcinoma are often single and inconspicuous in a background desmoplaia and inflammation, i…
Cancer is a leading cause of death in many countries. An early diagnosis of cancer based on biomedical imaging ensures effective treatment and a better prognosis. However, biomedical imaging presents challenges to both clinical institutions and researchers. Physiological anomalies are often characterized by slight abnormalities in individual cells or tissues, making them difficult to detect visually. Traditionally, anomalies are diagnosed by radiologists and pathologists with extensive training. This procedure, however, demands the participation of professionals and incurs a substantial cost. …
Multi-instance multi-label (MIML) learning is a challenging problem in many aspects. Such learning approaches might be useful for many medical diagnosis applications including breast cancer detection and classification. In this study subset of digiPATH dataset (whole slide digital breast cancer histopathology images) are used for training and evaluation of six state-of-the-art MIML methods. At the end, performance comparison of these approaches are given by means of effective evaluation metrics. It is shown that MIML-kNN achieve the best performance that is %65.3 average precision, where most …
DepViT-CAD: Deployable Vision Transformer-Based Cancer Diagnosis in Histopathology
University of Padova, Italy（意大利帕多瓦大学）；Unit of Surgical Pathology and Cytopathology, Ospedale dell’Angelo, Italy（意大利奥斯佩达莱-安吉洛外科病理学与细胞病理学单位）；Pathological Anatomy Unit, Padova University-Hospital, Italy（意大利帕多瓦大学医院病理学解剖单位）；Department of Medicine, University of Padova, Italy（意大利帕多瓦大学医学系）；Department of Integrated Diagnostics, Azienda Ospedale-Università Padova, Italy（意大利帕多瓦医院-大学整合诊断系）
Vietnam Aviation Academy（越南航空学院）；Ho Chi Minh City Open University（胡志明市开放大学）；University of Information Technology, VNU-HCM（胡志明市国家大学信息技术大学）
According to some medical imaging techniques, breast histopathology images called Hematoxylin and Eosin are considered as the gold standard for cancer diagnoses. Based on the idea of dividing the pathologic image (WSI) into multiple patches, we used the window [512,512] sliding from left to right and sliding from top to bottom, each sliding step overlapping by 50% to augmented data on a dataset of 400 images which were gathered from the ICIAR 2018 Grand Challenge. Then use the EffficientNet model to classify and identify the histopathological images of breast cancer into 4 types: Normal, Benig…
The MRI Institute for Biomedical Research（MRI 生物医学研究所）；Uber Technologies Inc.（优步技术公司）；LinkedIn（领英公司）；USAA（美国汽车协会联合服务银行）
A novel deep learning architecture (XmasNet) based on convolutional neural networks was developed for the classification of prostate cancer lesions, using the 3D multiparametric MRI data provided by the PROSTATEx challenge. End-to-end training was performed for XmasNet, with data augmentation done through 3D rotation and slicing, in order to incorporate the 3D information of the lesion. XmasNet outperformed traditional machine learning models based on engineered features, for both train and test data. For the test data, XmasNet outperformed 69 methods from 33 participating groups and achieved …
Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence Assisted Cancer Diagnosis
Karolinska Institutet（卡罗林斯卡学院）；FFEI Ltd（FFEI有限公司）；University of Turku（图尔库大学）；Stavanger University Hospital（斯塔万格大学医院）；University of Stavanger（斯塔万格大学）；Aarhus University Hospital（奥胡斯大学医院）；Aarhus University（奥胡斯大学）；Tampere University（坦佩雷大学）
Institute of Pathology, University Hospital Carl Gustav Carus (UKD), TU Dresden（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯大学医院病理研究所）；ASGEN GmbH & Co. KG（ASGEN两合有限责任公司）；National Center for Tumor Diseases (NCT), Partner Site Dresden（国家肿瘤疾病中心（德累斯顿合作站点））；ContextVision AB（康泰视伦公司）；Institute for Medical Informatics and Biometry (IMB), Carl Gustav Carus Faculty of Medicine, TU Dresden（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯医学院医学信息学与生物测量学研究所）；Institute of Bioengineering, School of Life Sciences, EPFL（洛桑联邦理工学院生命科学学院生物工程研究所）；Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG)（马克斯·普朗克分子细胞生物学与遗传学研究所）；Center for Systems Biology Dresden (CSBD)（德累斯顿系统生物学中心）
Histo-pathological diagnostics are an inherent part of the everyday work but are particularly laborious and associated with time-consuming manual analysis of image data. In order to cope with the increasing diagnostic case numbers due to the current growth and demographic change of the global population and the progress in personalized medicine, pathologists ask for assistance. Profiting from digital pathology and the use of artificial intelligence, individual solutions can be offered (e.g. detect labeled cancer tissue sections). The testing of the human epidermal growth factor receptor 2 (HER…
We introduce a new computer aided detection and diagnosis system for lung cancer screening with low-dose CT scans that produces meaningful probability assessments. Our system is based entirely on 3D convolutional neural networks and achieves state-of-the-art performance for both lung nodule detection and malignancy classification tasks on the publicly available LUNA16 and Kaggle Data Science Bowl challenges. While nodule detection systems are typically designed and optimized on their own, we find that it is important to consider the coupling between detection and diagnosis components. Exploiti…
Department of Medicine I, LMU University Hospital, LMU Munich, Germany（慕尼黑大学医学部第一部门，LMU大学医院，慕尼黑，德国）；Lunit Inc.（Lunit公司）
Large annotated datasets are essential for training robust Computer-Aided Diagnosis (CAD) models for breast cancer detection or risk prediction. However, acquiring such datasets with fine-detailed annotation is both costly and time-consuming. Vision-Language Models (VLMs), such as CLIP, which are pre-trained on large image-text pairs, offer a promising solution by enhancing robustness and data efficiency in medical imaging tasks. This paper introduces a novel Multi-View Mammography and Language Model for breast cancer classification and risk prediction, trained on a dataset of paired mammogram…
Shanghai Institute for Advanced Communication and Data Science（上海先进通信与数据科学研究院）；The First Affiliated Hospital of Anhui Medical University（安徽医科大学第一附属医院）；Fudan University Shanghai Cancer Center（复旦大学附属肿瘤医院）；Shanghai Medical College, Fudan University（复旦大学上海医学院）；School of Science, Shanghai University（上海大学理学院）
Elastography ultrasound (EUS) provides additional bio-mechanical in-formation about lesion for B-mode ultrasound (BUS) in the diagnosis of breast cancers. However, joint utilization of both BUS and EUS is not popular due to the lack of EUS devices in rural hospitals, which arouses a novel modality im-balance problem in computer-aided diagnosis (CAD) for breast cancers. Current transfer learning (TL) pay little attention to this special issue of clinical modality imbalance, that is, the source domain (EUS modality) has fewer labeled samples than those in the target domain (BUS modality). Moreov…
Medmain Inc.（Medmain公司）；Sapporo Kosei General Hospital（札幌厚生综合医院）；Hiroshima University Hospital（广岛大学医院）
Signet ring cell carcinoma (SRCC) of the stomach is a rare type of cancer with a slowly rising incidence. It tends to be more difficult to detect by pathologists mainly due to its cellular morphology and diffuse invasion manner, and it has poor prognosis when detected at an advanced stage. Computational pathology tools that can assist pathologists in detecting SRCC would be of a massive benefit. In this paper, we trained deep learning models using transfer learning, fully-supervised learning, and weakly-supervised learning to predict SRCC in Whole Slide Images (WSIs) using a training set of 1,…
The University of Tokyo Hospital（东京大学医院）；Graduate School of Medicine, The University of Tokyo（东京大学医学院）；Junpukai Health Maintenance Center（顺会健康体检中心）；Hokkaido University（北海道大学）
Today, gastric cancer is one of the diseases which affected many people's life. Early detection and accuracy are the main and crucial challenges in finding this kind of cancer. In this paper, a method to increase the accuracy of the diagnosis of detecting cancer using lint and colour features of tongue based on deep convolutional neural networks and support vector machine is proposed. In the proposed method, the region of tongue is first separated from the face image by {deep RCNN} \color{black} Recursive Convolutional Neural Network (R-CNN) \color{black}. After the necessary preprocessing, th…
Thyroid cancer is common worldwide, with a rapid increase in prevalence across North America in recent years. While most patients present with palpable nodules through physical examination, a large number of small and medium-sized nodules are detected by ultrasound examination. Suspicious nodules are then sent for biopsy through fine needle aspiration. Since biopsies are invasive and sometimes inconclusive, various research groups have tried to develop computer-aided diagnosis systems. Earlier approaches along these lines relied on clinically relevant features that were manually identified by …
NMAM Institute of Technology（NMAM理工学院）；Visvesvaraya Technological University（韦斯韦斯瓦拉亚理工大学）；Sahyadri College of Engineering and Management（萨亚德里工程与管理学院）；JSS Academy of Technical Education（JSS技术教育学院）
Lung cancer begins in the lungs and leading to the reason of cancer demise amid population in the creation. According to the American Cancer Society, which estimates about 27% of the deaths because of cancer. In the early phase of its evolution, lung cancer does not cause any symptoms usually. Many of the patients have been diagnosed in a developed phase where symptoms become more prominent, that results in poor curative treatment and high mortality rate. Computer Aided Detection systems are used to achieve greater accuracies for the lung cancer diagnosis. In this research exertion, we propose…
South China University of Technology（华南理工大学）；Guangdong Provincial People’s Hospital（广东省人民医院）；Guangdong Academy of Medical Sciences（广东省医学科学院）
Ultrasonography is an important routine examination for breast cancer diagnosis, due to its non-invasive, radiation-free and low-cost properties. However, the diagnostic accuracy of breast cancer is still limited due to its inherent limitations. It would be a tremendous success if we can precisely diagnose breast cancer by breast ultrasound images (BUS). Many learning-based computer-aided diagnostic methods have been proposed to achieve breast cancer diagnosis/lesion classification. However, most of them require a pre-define ROI and then classify the lesion inside the ROI. Conventional classif…
Maastricht University Medical Centre+（马斯特里赫特大学医学中心+）；GROW School for Oncology and Developmental Biology（GROW肿瘤学与发育生物学学院）
Early diagnosis of lung cancer is a key intervention for the treatment of lung cancer computer aided diagnosis (CAD) can play a crucial role. However, most published CAD methods treat lung cancer diagnosis as a lung nodule classification problem, which does not reflect clinical practice, where clinicians diagnose a patient based on a set of images of nodules, instead of one specific nodule. Besides, the low interpretability of the output provided by these methods presents an important barrier for their adoption. In this article, we treat lung cancer diagnosis as a multiple instance learning (M…
Using Computer Vision for Skin Disease Diagnosis in Bangladesh Enhancing Interpretability and Transparency in Deep Learning Models for Skin Cancer Classification
University of Idaho（爱达荷大学）；Harbin Institute of Technology（哈尔滨工业大学）；Medical College at Qingdao University（青岛大学医学院）
Deep learning-based computer-aided diagnosis has achieved unprecedented performance in breast cancer detection. However, most approaches are computationally intensive, which impedes their broader dissemination in real-world applications. In this work, we propose an efficient and light-weighted multitask learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. Moreover, we propose a new numerically stable loss function that e…
Deep learning can promote the mammography-based computer-aided diagnosis (CAD) for breast cancers, but it generally suffers from the small sample size problem. Self-supervised learning (SSL) has shown its effectiveness in medical image analysis with limited training samples. However, the network model sometimes cannot be well pre-trained in the conventional SSL framework due to the limitation of the pretext task and fine-tuning mechanism. In this work, a Task-driven Self-supervised Bi-channel Networks (TSBN) framework is proposed to improve the performance of classification model the mammograp…
University of Minnesota（明尼苏达大学）；University of Cincinnati（辛辛那提大学）
Image classification is widely used to build predictive models for breast cancer diagnosis. Most existing approaches overwhelmingly rely on deep convolutional networks to build such diagnosis pipelines. These model architectures, although remarkable in performance, are black-box systems that provide minimal insight into the inner logic behind their predictions. This is a major drawback as the explainability of prediction is vital for applications such as cancer diagnosis. In this paper, we address this issue by proposing an explainable machine learning pipeline for breast cancer diagnosis base…
FEMTO-ST institute（FEMTO-ST研究所）；Univ. Bourgogne Franche-Comté（勃艮第-弗朗什-孔泰大学）；CNRS（法国国家科学研究中心）；ENSMM（国立高等机械与微技术学院）；University of Oviedo（奥维耶多大学）；Universidad Autonoma del Caribe（加勒比自治大学）；Batna University（巴特纳大学）；University of Biskra（比斯克拉大学）
Micro Abstract: A recent study from GLOBOCAN disclosed that during 2018 two million women worldwide had been diagnosed from breast cancer. This study presents a computer-aided diagnosis system based on convolutional neural networks as an alternative diagnosis methodology for breast cancer diagnosis with thermal images. Experimental results showed that lower false-positives and false-negatives classification rates are obtained when data pre-processing and data augmentation techniques are implemented in these thermal images. Background: There are many types of breast cancer screening techniques …
University of Saskatchewan（萨斯喀彻温大学）；University of Tulane（杜兰大学）
Breast cancer is one of the most common causes of cancer-related death in women worldwide. Early and accurate diagnosis of breast cancer may significantly increase the survival rate of patients. In this study, we aim to develop a fully automatic, deep learning-based, method using descriptor features extracted by Deep Convolutional Neural Network (DCNN) models and pooling operation for the classification of hematoxylin and eosin stain (H&E) histological breast cancer images provided as a part of the International Conference on Image Analysis and Recognition (ICIAR) 2018 Grand Challenge on BreAs…
Amity School of Engineering and Technology, Amity University（阿米提大学阿米提工程技术学院）；Dr. Ram Manohar Lohia Awadh University（拉姆·马诺哈尔·洛希亚博士阿瓦德大学）
Computer Aided Diagnosis has emerged as an indispensible technique for validating the opinion of radiologists in CT interpretation. This paper presents a deep 3D Convolutional Neural Network (CNN) architecture for automated CT scan-based lung cancer detection system. It utilizes three dimensional spatial information to learn highly discriminative 3 dimensional features instead of 2D features like texture or geometric shape whick need to be generated manually. The proposed deep learning method automatically extracts the 3D features on the basis of spatio-temporal statistics.The developed model …
