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Book ; Online: HAT-CL

Duan, Xiaotian

A Hard-Attention-to-the-Task PyTorch Library for Continual Learning

2023

Abstract: Catastrophic forgetting, the phenomenon in which a neural network loses previously obtained knowledge during the learning of new tasks, poses a significant challenge in continual learning. The Hard-Attention-to-the-Task (HAT) mechanism has shown ... ...

Abstract	Catastrophic forgetting, the phenomenon in which a neural network loses previously obtained knowledge during the learning of new tasks, poses a significant challenge in continual learning. The Hard-Attention-to-the-Task (HAT) mechanism has shown potential in mitigating this problem, but its practical implementation has been complicated by issues of usability and compatibility, and a lack of support for existing network reuse. In this paper, we introduce HAT-CL, a user-friendly, PyTorch-compatible redesign of the HAT mechanism. HAT-CL not only automates gradient manipulation but also streamlines the transformation of PyTorch modules into HAT modules. It achieves this by providing a comprehensive suite of modules that can be seamlessly integrated into existing architectures. Additionally, HAT-CL offers ready-to-use HAT networks that are smoothly integrated with the TIMM library. Beyond the redesign and reimplementation of HAT, we also introduce novel mask manipulation techniques for HAT, which have consistently shown improvements across various experiments. Our work paves the way for a broader application of the HAT mechanism, opening up new possibilities in continual learning across diverse models and applications.
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
Subject code	004
Publishing date	2023-07-18
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Regression Enrichment Surfaces

Clyde, Austin / Duan, Xiaotian / Stevens, Rick

a Simple Analysis Technique for Virtual Drug Screening Models

2020

Abstract: We present a new method for understanding the performance of a model in virtual drug screening tasks. While most virtual screening problems present as a mix between ranking and classification, the models are typically trained as regression models ... ...

Abstract	We present a new method for understanding the performance of a model in virtual drug screening tasks. While most virtual screening problems present as a mix between ranking and classification, the models are typically trained as regression models presenting a problem requiring either a choice of a cutoff or ranking measure. Our method, regression enrichment surfaces (RES), is based on the goal of virtual screening: to detect as many of the top-performing treatments as possible. We outline history of virtual screening performance measures and the idea behind RES. We offer a python package and details on how to implement and interpret the results.
Keywords	Quantitative Biology - Quantitative Methods ; Computer Science - Machine Learning ; Statistics - Machine Learning
Publishing date	2020-06-01
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Four distinct isolates of Helminthosporium victoriae virus 190S identified from Bipolaris maydis

Wu, Ruixue / Yang, Yunyue / Duan, Xiaotian / An, Hongliu / Du, Zhenguo / Zhang, Songbai / Zhang, Xiaoting

Virus research. 2020 Aug., v. 285

2020

Abstract: Helminthosporium victoriae virus 190S (HvV190S) is the type species of the genus Victorivirus under the family Totiviridae. To date, HvV190S has never been found in places outside of the USA and has Helminthosporium victoriae as its only know natural ... ...

Abstract	Helminthosporium victoriae virus 190S (HvV190S) is the type species of the genus Victorivirus under the family Totiviridae. To date, HvV190S has never been found in places outside of the USA and has Helminthosporium victoriae as its only know natural host fungus in the field. Here, we report the identification of 4 double-stranded RNA (dsRNA) viruses from Bipolaris maydis in Hubei province of China. Interestingly, the genomes of the 4 viruses show 81.2 %–85.5 % nucleotide sequence identities to HvV190S. Their capsid protein (CP) and RNA-dependent RNA polymerase (RdRp) share 95.5–97.9 % and 94.6–96.6 % amino acid sequence identities to corresponding proteins of HvV190S. Therefore, the 4 viruses, which show 81.8–87.3 % pairwise genome sequence identities, should be considered as distinct isolates of HvV190S. Our finding suggests that HvV190S is widely distributed in the world and may infect fungal species other than H. victoriae.
Keywords	Bipolaris maydis ; Bipolaris victoriae ; Helminthosporium victoriae virus 190S ; RNA-directed RNA polymerase ; amino acid sequences ; coat proteins ; double-stranded RNA ; fungi ; genome ; nucleotide sequences ; viruses ; China
Language	English
Dates of publication	2020-08
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	605780-9
ISSN	1872-7492 ; 0168-1702
ISSN (online)	1872-7492
ISSN	0168-1702
DOI	10.1016/j.virusres.2020.197941
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Four distinct isolates of Helminthosporium victoriae virus 190S identified from Bipolaris maydis.

Wu, Ruixue / Yang, Yunyue / Duan, Xiaotian / An, Hongliu / Du, Zhenguo / Zhang, Songbai / Zhang, Xiaoting

Virus research

2020 Volume 285, Page(s) 197941

Abstract	Helminthosporium victoriae virus 190S (HvV190S) is the type species of the genus Victorivirus under the family Totiviridae. To date, HvV190S has never been found in places outside of the USA and has Helminthosporium victoriae as its only know natural host fungus in the field. Here, we report the identification of 4 double-stranded RNA (dsRNA) viruses from Bipolaris maydis in Hubei province of China. Interestingly, the genomes of the 4 viruses show 81.2 %-85.5 % nucleotide sequence identities to HvV190S. Their capsid protein (CP) and RNA-dependent RNA polymerase (RdRp) share 95.5-97.9 % and 94.6-96.6 % amino acid sequence identities to corresponding proteins of HvV190S. Therefore, the 4 viruses, which show 81.8-87.3 % pairwise genome sequence identities, should be considered as distinct isolates of HvV190S. Our finding suggests that HvV190S is widely distributed in the world and may infect fungal species other than H. victoriae.
MeSH term(s)	Bipolaris/virology ; Capsid Proteins/genetics ; China ; Genome, Viral ; RNA, Double-Stranded ; RNA, Viral ; RNA-Dependent RNA Polymerase/genetics ; Totiviridae/isolation & purification
Chemical Substances	Capsid Proteins ; RNA, Double-Stranded ; RNA, Viral ; RNA-Dependent RNA Polymerase (EC 2.7.7.48)
Language	English
Publishing date	2020-05-06
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	605780-9
ISSN	1872-7492 ; 0168-1702
ISSN (online)	1872-7492
ISSN	0168-1702
DOI	10.1016/j.virusres.2020.197941
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Article ; Online: A cross-study analysis of drug response prediction in cancer cell lines.

Briefings in bioinformatics

2021 Volume 23, Issue 1

Abstract: To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm development efforts have relied on cross-validation within a single study to ... ...

Abstract	To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm development efforts have relied on cross-validation within a single study to assess model accuracy. While an essential first step, cross-validation within a biological data set typically provides an overly optimistic estimate of the prediction performance on independent test sets. To provide a more rigorous assessment of model generalizability between different studies, we use machine learning to analyze five publicly available cell line-based data sets: National Cancer Institute 60, ancer Therapeutics Response Portal (CTRP), Genomics of Drug Sensitivity in Cancer, Cancer Cell Line Encyclopedia and Genentech Cell Line Screening Initiative (gCSI). Based on observed experimental variability across studies, we explore estimates of prediction upper bounds. We report performance results of a variety of machine learning models, with a multitasking deep neural network achieving the best cross-study generalizability. By multiple measures, models trained on CTRP yield the most accurate predictions on the remaining testing data, and gCSI is the most predictable among the cell line data sets included in this study. With these experiments and further simulations on partial data, two lessons emerge: (1) differences in viability assays can limit model generalizability across studies and (2) drug diversity, more than tumor diversity, is crucial for raising model generalizability in preclinical screening.
MeSH term(s)	Algorithms ; Cell Line ; Humans ; Machine Learning ; Neoplasms/drug therapy ; Neoplasms/genetics ; Neural Networks, Computer
Language	English
Publishing date	2021-09-15
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbab356
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: A cross-study analysis of drug response prediction in cancer cell lines

2021

Abstract	To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm development efforts have relied on cross validation within a single study to assess model accuracy. While an essential first step, cross validation within a biological data set typically provides an overly optimistic estimate of the prediction performance on independent test sets. To provide a more rigorous assessment of model generalizability between different studies, we use machine learning to analyze five publicly available cell line-based data sets: NCI60, CTRP, GDSC, CCLE and gCSI. Based on observed experimental variability across studies, we explore estimates of prediction upper bounds. We report performance results of a variety of machine learning models, with a multitasking deep neural network achieving the best cross-study generalizability. By multiple measures, models trained on CTRP yield the most accurate predictions on the remaining testing data, and gCSI is the most predictable among the cell line data sets included in this study. With these experiments and further simulations on partial data, two lessons emerge: (1) differences in viability assays can limit model generalizability across studies, and (2) drug diversity, more than tumor diversity, is crucial for raising model generalizability in preclinical screening. Comment: Accepted by Briefings in Bioinformatics
Keywords	Quantitative Biology - Quantitative Methods
Publishing date	2021-04-18
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: CholecTriplet2021: A benchmark challenge for surgical action triplet recognition.

Nwoye, Chinedu Innocent / Alapatt, Deepak / Yu, Tong / Vardazaryan, Armine / Xia, Fangfang / Zhao, Zixuan / Xia, Tong / Jia, Fucang / Yang, Yuxuan / Wang, Hao / Yu, Derong / Zheng, Guoyan / Duan, Xiaotian / Getty, Neil / Sanchez-Matilla, Ricardo / Robu, Maria / Zhang, Li / Chen, Huabin / Wang, Jiacheng /

Wang, Liansheng / Zhang, Bokai / Gerats, Beerend / Raviteja, Sista / Sathish, Rachana / Tao, Rong / Kondo, Satoshi / Pang, Winnie / Ren, Hongliang / Abbing, Julian Ronald / Sarhan, Mohammad Hasan / Bodenstedt, Sebastian / Bhasker, Nithya / Oliveira, Bruno / Torres, Helena R / Ling, Li / Gaida, Finn / Czempiel, Tobias / Vilaça, João L / Morais, Pedro / Fonseca, Jaime / Egging, Ruby Mae / Wijma, Inge Nicole / Qian, Chen / Bian, Guibin / Li, Zhen / Balasubramanian, Velmurugan / Sheet, Debdoot / Luengo, Imanol / Zhu, Yuanbo / Ding, Shuai / Aschenbrenner, Jakob-Anton / van der Kar, Nicolas Elini / Xu, Mengya / Islam, Mobarakol / Seenivasan, Lalithkumar / Jenke, Alexander / Stoyanov, Danail / Mutter, Didier / Mascagni, Pietro / Seeliger, Barbara / Gonzalez, Cristians / Padoy, Nicolas

Medical image analysis

2023 Volume 86, Page(s) 102803

Abstract: Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, ... ...

Abstract	Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity; yet those are needed for more helpful AI assistance in the operating room. Recognizing surgical actions as triplets of ‹instrument, verb, target› combination delivers more comprehensive details about the activities taking place in surgical videos. This paper presents CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. The challenge granted private access to the large-scale CholecT50 dataset, which is annotated with action triplet information. In this paper, we present the challenge setup and the assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. A total of 4 baseline methods from the challenge organizers and 19 new deep learning algorithms from the competing teams are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%. This study also analyzes the significance of the results obtained by the presented approaches, performs a thorough methodological comparison between them, in-depth result analysis, and proposes a novel ensemble method for enhanced recognition. Our analysis shows that surgical workflow analysis is not yet solved, and also highlights interesting directions for future research on fine-grained surgical activity recognition which is of utmost importance for the development of AI in surgery.
MeSH term(s)	Humans ; Algorithms ; Benchmarking ; Laparoscopy ; Operating Rooms ; Workflow ; Deep Learning
Language	English
Publishing date	2023-03-27
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1356436-5
ISSN	1361-8423 ; 1361-8431 ; 1361-8415
ISSN (online)	1361-8423 ; 1361-8431
ISSN	1361-8415
DOI	10.1016/j.media.2023.102803
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Book ; Online: Surgical tool classification and localization

Zia, Aneeq / Bhattacharyya, Kiran / Liu, Xi / Berniker, Max / Wang, Ziheng / Nespolo, Rogerio / Kondo, Satoshi / Kasai, Satoshi / Hirasawa, Kousuke / Liu, Bo / Austin, David / Wang, Yiheng / Futrega, Michal / Puget, Jean-Francois / Li, Zhenqiang / Sato, Yoichi / Fujii, Ryo / Hachiuma, Ryo / Masuda, Mana /

Saito, Hideo / Wang, An / Xu, Mengya / Islam, Mobarakol / Bai, Long / Pang, Winnie / Ren, Hongliang / Nwoye, Chinedu / Sestini, Luca / Padoy, Nicolas / Nielsen, Maximilian / Schüttler, Samuel / Sentker, Thilo / Husseini, Hümeyra / Baltruschat, Ivo / Schmitz, Rüdiger / Werner, René / Matsun, Aleksandr / Farooq, Mugariya / Saaed, Numan / Viera, Jose Renato Restom / Yaqub, Mohammad / Getty, Neil / Xia, Fangfang / Zhao, Zixuan / Duan, Xiaotian / Yao, Xing / Lou, Ange / Yang, Hao / Han, Jintong / Noble, Jack / Wu, Jie Ying / Alshirbaji, Tamer Abdulbaki / Jalal, Nour Aldeen / Arabian, Herag / Ding, Ning / Moeller, Knut / Chen, Weiliang / He, Quan / Bilal, Muhammad / Akinosho, Taofeek / Qayyum, Adnan / Caputo, Massimo / Vohra, Hunaid / Loizou, Michael / Ajayi, Anuoluwapo / Berrou, Ilhem / Niyi-Odumosu, Faatihah / Maier-Hein, Lena / Stoyanov, Danail / Speidel, Stefanie / Jarc, Anthony

results and methods from the MICCAI 2022 SurgToolLoc challenge

2023

Abstract: The ability to automatically detect and track surgical instruments in endoscopic videos can enable transformational interventions. Assessing surgical performance and efficiency, identifying skilled tool use and choreography, and planning operational and ... ...

Abstract	The ability to automatically detect and track surgical instruments in endoscopic videos can enable transformational interventions. Assessing surgical performance and efficiency, identifying skilled tool use and choreography, and planning operational and logistical aspects of OR resources are just a few of the applications that could benefit. Unfortunately, obtaining the annotations needed to train machine learning models to identify and localize surgical tools is a difficult task. Annotating bounding boxes frame-by-frame is tedious and time-consuming, yet large amounts of data with a wide variety of surgical tools and surgeries must be captured for robust training. Moreover, ongoing annotator training is needed to stay up to date with surgical instrument innovation. In robotic-assisted surgery, however, potentially informative data like timestamps of instrument installation and removal can be programmatically harvested. The ability to rely on tool installation data alone would significantly reduce the workload to train robust tool-tracking models. With this motivation in mind we invited the surgical data science community to participate in the challenge, SurgToolLoc 2022. The goal was to leverage tool presence data as weak labels for machine learning models trained to detect tools and localize them in video frames with bounding boxes. We present the results of this challenge along with many of the team's efforts. We conclude by discussing these results in the broader context of machine learning and surgical data science. The training data used for this challenge consisting of 24,695 video clips with tool presence labels is also being released publicly and can be accessed at https://console.cloud.google.com/storage/browser/isi-surgtoolloc-2022.
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	670
Publishing date	2023-05-11
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: CholecTriplet2021

Wang, Liansheng / Zhang, Bokai / Gerats, Beerend / Raviteja, Sista / Sathish, Rachana / Tao, Rong / Kondo, Satoshi / Pang, Winnie / Ren, Hongliang / Abbing, Julian Ronald / Sarhan, Mohammad Hasan / Bodenstedt, Sebastian / Bhasker, Nithya / Oliveira, Bruno / Torres, Helena R. / Ling, Li / Gaida, Finn / Czempiel, Tobias / Vilaça, João L. / Morais, Pedro / Fonseca, Jaime / Egging, Ruby Mae / Wijma, Inge Nicole / Qian, Chen / Bian, Guibin / Li, Zhen / Balasubramanian, Velmurugan / Sheet, Debdoot / Luengo, Imanol / Zhu, Yuanbo / Ding, Shuai / Aschenbrenner, Jakob-Anton / van der Kar, Nicolas Elini / Xu, Mengya / Islam, Mobarakol / Seenivasan, Lalithkumar / Jenke, Alexander / Stoyanov, Danail / Mutter, Didier / Mascagni, Pietro / Seeliger, Barbara / Gonzalez, Cristians / Padoy, Nicolas

A benchmark challenge for surgical action triplet recognition

2022

Abstract	Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity; yet those are needed for more helpful AI assistance in the operating room. Recognizing surgical actions as triplets of combination delivers comprehensive details about the activities taking place in surgical videos. This paper presents CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. The challenge granted private access to the large-scale CholecT50 dataset, which is annotated with action triplet information. In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. A total of 4 baseline methods from the challenge organizers and 19 new deep learning algorithms by competing teams are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%. This study also analyzes the significance of the results obtained by the presented approaches, performs a thorough methodological comparison between them, in-depth result analysis, and proposes a novel ensemble method for enhanced recognition. Our analysis shows that surgical workflow analysis is not yet solved, and also highlights interesting directions for future research on fine-grained surgical activity recognition which is of utmost importance for the development of AI in surgery. Comment: CholecTriplet2021 challenge report. Paper accepted at Elsevier journal of Medical Image Analysis. 22 pages, 8 figures, 11 tables. Challenge website: https://cholectriplet2021.grand-challenge.org
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2022-04-10
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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