Machine learning (ML) continues to grow in importance across nearly all domains and is a natural tool in modeling to learn from data. Often a tradeoff exists between a model's ability to minimize bias and variance. In this paper, we utilize ensemble learning to combine linear, nonlinear, and tree-/rule-based ML methods to cope with the bias-variance tradeoff and result in more accurate models. Hardware performance counter values are correlated with properties of applications that impact performance and power on the underlying system. We use the datasets collected for two parallel cancer deep learning CANDLE benchmarks, NT3 (weak scaling) and P1B2 (strong scaling), to build performance and power models based on hardware performance counters using single-object and multiple-objects ensemble learning to identify the most important counters for improvement. Based on the insights from these models, we improve the performance and energy of P1B2 and NT3 by optimizing the deep learning environments TensorFlow, Keras, Horovod, and Python under the huge page size of 8 MB on the Cray XC40 Theta at Argonne National Laboratory. Experimental results show that ensemble learning not only produces more accurate models but also provides more robust performance counter ranking. We achieve up to 61.15% performance improvement and up to 62.58% energy saving for P1B2 and up to 55.81% performance improvement and up to 52.60% energy saving for NT3 on up to 24,576 cores.
Drug Repurposing for Cancer: An NLP Approach to Identify Low-Cost Therapies
IBM Research（IBM研究院）；University of Melbourne（墨尔本大学）；Cures Within Reach for Cancer（癌症可及疗法组织）；Massachusetts Institute of Technology（麻省理工学院）；Harvard Medical School（哈佛医学院）
DiagSet: a dataset for prostate cancer histopathological image classification
Diagnostyka Consilio Sp. z o.o.（诊断康西利奥有限公司）；AGH University of Science and Technology（AGH科技大学）；Andrzej Frycz Modrzewski Krakow University（安德烈·弗赖奇·莫德热夫斯基克拉科夫大学）
Nagoya University（名古屋大学）；Nagoya University Graduate School of Medicine（名古屋大学医学研究科）
This paper proposes a novel, unsupervised super-resolution (SR) approach for performing the SR of a clinical CT into the resolution level of a micro CT ($μ$CT). The precise non-invasive diagnosis of lung cancer typically utilizes clinical CT data. Due to the resolution limitations of clinical CT (about $0.5 \times 0.5 \times 0.5$ mm$^3$), it is difficult to obtain enough pathological information such as the invasion area at alveoli level. On the other hand, $μ$CT scanning allows the acquisition of volumes of lung specimens with much higher resolution ($50 \times 50 \times 50 μ{\rm m}^3$ or hig…
Uppsala University（乌普萨拉大学）；Pathology and Cytology Dalarna, County Hospital Falun（法伦县医院达拉纳病理与细胞学）；Clinical Research Center Dalarna, Uppsala University（乌普萨拉大学达拉纳临床研究中心）
The current medical standard for setting an oral cancer (OC) diagnosis is histological examination of a tissue sample from the oral cavity. This process is time consuming and more invasive than an alternative approach of acquiring a brush sample followed by cytological analysis. Skilled cytotechnologists are able to detect changes due to malignancy, however, to introduce this approach into clinical routine is associated with challenges such as a lack of experts and labour-intensive work. To design a trustworthy OC detection system that would assist cytotechnologists, we are interested in AI-ba…
Jaypee University of Information Technology（贾皮尔信息技术大学）
Colon and Lung cancer is one of the most perilous and dangerous ailments that individuals are enduring worldwide and has become a general medical problem. To lessen the risk of death, a legitimate and early finding is particularly required. In any case, it is a truly troublesome task that depends on the experience of histopathologists. If a histologist is under-prepared it may even hazard the life of a patient. As of late, deep learning has picked up energy, and it is being valued in the analysis of Medical Imaging. This paper intends to utilize and alter the current pre-trained CNN-based mode…
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；New York University（纽约大学）
In this study, with the goal of reducing the early detection miss rate of colorectal cancer (CRC) polyps, we propose utilizing a novel hyper-sensitive vision-based tactile sensor called HySenSe and a complementary and novel machine learning (ML) architecture that explores the potentials of utilizing dilated convolutions, the beneficial features of the ResNet architecture, and the transfer learning concept applied on a small dataset with the scale of hundreds of images. The proposed tactile sensor provides high-resolution 3D textural images of CRC polyps that will be used for their accurate cla…
Eindhoven University of Technology（埃因霍温理工大学）；Catharina Ziekenhuis（凯瑟琳娜医院）；Philips Research（飞利浦研究院）
Pancreatic cancer is one of the global leading causes of cancer-related deaths. Despite the success of Deep Learning in computer-aided diagnosis and detection (CAD) methods, little attention has been paid to the detection of Pancreatic Cancer. We propose a method for detecting pancreatic tumor that utilizes clinically-relevant features in the surrounding anatomical structures, thereby better aiming to exploit the radiologist's knowledge compared to other, conventional deep learning approaches. To this end, we collect a new dataset consisting of 99 cases with pancreatic ductal adenocarcinoma (P…
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization
Center for Data Science, New York University（纽约大学数据科学中心）；Department of Computer Science, Courant Institute, New York University（纽约大学库朗研究所计算机科学系）；Department of Radiology, NYU School of Medicine（纽约大学医学院放射科）；Perlmutter Cancer Center, NYU Langone Health（纽约大学朗格尼健康系统珀尔马特癌症中心）；Center for Advanced Imaging Innovation and Research, NYU Langone Health（纽约大学朗格尼健康系统先进成像创新与研究中心）；CIFAR Associate Fellow（CIFAR研究员（注：CIFAR是加拿大英属哥伦比亚大学的一个研究机构，但根据提取规则，此处仅提取“CIFAR”相关部分，严格按英文保留））
Argonne National Laboratory（阿贡国家实验室）；The University of Chicago（芝加哥大学）
Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans, ultimately suppressing tumors, alleviating suffering, and prolonging lives of patients. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. These papers investigate diverse data representations, n…
Predicting clinical outcomes to anti-cancer drugs on a personalized basis is challenging in cancer treatment due to the heterogeneity of tumors. Traditional computational efforts have been made to model the effect of drug response on individual samples depicted by their molecular profile, yet overfitting occurs because of the high dimension for omics data, hindering models from clinical application. Recent research shows that deep learning is a promising approach to build drug response models by learning alignment patterns between drugs and samples. However, existing studies employed the simpl…
Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer
University of Augsburg（奥格斯堡大学）；University Hospital Augsburg（奥格斯堡大学医院）；Neu-Ulm University of Applied Sciences（新乌尔姆应用科学大学）；Bavarian Cancer Research Center（巴伐利亚癌症研究中心）；BioM Biotech Cluster Development GmbH（BioM生物技术集群发展有限公司）
Slovak University of Technology（斯洛伐克工业大学）；Institute for Clinical and Experimental Medicine（临床与实验医学研究所）；Charles University（查理大学）；Thomayer University Hospital（托马耶大学医院）
Prevention and early diagnosis of breast cancer (BC) is an essential prerequisite for the selection of proper treatment. The substantial pressure due to the increase of demand for faster and more precise diagnostic results drives for automatic solutions. In the past decade, deep learning techniques have demonstrated their power over several domains, and Computer-Aided (CAD) diagnostic became one of them. However, when it comes to the analysis of Whole Slide Images (WSI), most of the existing works compute predictions from levels independently. This is, however, in contrast to the histopatholog…
Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling
University of California, Los Angeles（加州大学洛杉矶分校）；California NanoSystems Institute（加州纳米系统研究所）；University of California at Davis（加州大学戴维斯分校）；Bnai-Zion Medical Center（贝内锡安医疗中心）；Hadassah Hebrew University Medical Center（哈达萨希伯来大学医疗中心）；David Geffen School of Medicine, University of California, Los Angeles（加州大学洛杉矶分校大卫·格芬医学院）
Self-learned representation-guided latent diffusion model for breast cancer classification in deep ultraviolet whole surface images
Department of Computer Science, Georgia State University（计算机科学系，佐治亚州立大学）；Department of Electrical and Computer Engineering, Marquette University（电气与计算机工程系，马基特大学）；Joint Dept. of Biomedical Eng., Marquette Univ. and Med. Coll. of Wisconsin（马基特大学与威斯康星医学院联合生物医学工程系）
DeepGleason: a System for Automated Gleason Grading of Prostate Cancer using Deep Neural Networks
University of Augsburg（奥格斯堡大学）；University Hospital Augsburg（奥格斯堡大学医院）；BioM Biotech Cluster Development GmbH（BioM生物技术集群发展有限公司）；Bavarian Cancer Research Center (BZKF)（巴伐利亚癌症研究中心）
