TY - JOUR
T1 - Multitask Learning in Minimally Invasive Surgical Vision: A Review
AU - Alabi, Tosin
AU - Vercauteren, Tom
AU - Shi, Miaojing
N1 - Publisher Copyright:
© 2025 Elsevier B.V.
PY - 2025/4
Y1 - 2025/4
N2 - Minimally invasive surgery (MIS) has revolutionized many procedures and led to reduced recovery time and risk of patient injury. However, MIS poses additional complexity and burden on surgical teams. Data-driven surgical vision algorithms are thought to be key building blocks in the development of future MIS systems with improved autonomy. Recent advancements in machine learning and computer vision have led to successful applications in analysing videos obtained from MIS with the promise of alleviating challenges in MIS videos. Surgical scene and action understanding encompasses multiple related tasks that, when solved individually, can be memory-intensive, inefficient, and fail to capture task relationships. Multitask learning (MTL), a learning paradigm that leverages information from multiple related tasks to improve performance and aid generalization, is well-suited for fine-grained and high-level understanding of MIS data. This review provides a narrative overview of the current state-of-the-art MTL systems that leverage videos obtained from MIS. Beyond listing published approaches, we discuss the benefits and limitations of these MTL systems. Moreover, this manuscript presents an analysis of the literature for various application fields of MTL in MIS, including those with large models, highlighting notable trends, new directions of research, and developments.
AB - Minimally invasive surgery (MIS) has revolutionized many procedures and led to reduced recovery time and risk of patient injury. However, MIS poses additional complexity and burden on surgical teams. Data-driven surgical vision algorithms are thought to be key building blocks in the development of future MIS systems with improved autonomy. Recent advancements in machine learning and computer vision have led to successful applications in analysing videos obtained from MIS with the promise of alleviating challenges in MIS videos. Surgical scene and action understanding encompasses multiple related tasks that, when solved individually, can be memory-intensive, inefficient, and fail to capture task relationships. Multitask learning (MTL), a learning paradigm that leverages information from multiple related tasks to improve performance and aid generalization, is well-suited for fine-grained and high-level understanding of MIS data. This review provides a narrative overview of the current state-of-the-art MTL systems that leverage videos obtained from MIS. Beyond listing published approaches, we discuss the benefits and limitations of these MTL systems. Moreover, this manuscript presents an analysis of the literature for various application fields of MTL in MIS, including those with large models, highlighting notable trends, new directions of research, and developments.
KW - Computer aided intervention
KW - Multitask learning
KW - Minimally invasive surgeries
UR - http://www.scopus.com/inward/record.url?scp=85217276018&partnerID=8YFLogxK
U2 - 10.1016/j.media.2025.103480
DO - 10.1016/j.media.2025.103480
M3 - Review article
SN - 1361-8415
VL - 101
JO - Medical Image Analysis
JF - Medical Image Analysis
M1 - 103480
ER -