  Following the technological advancements in medicine, the operation rooms are
evolving into intelligent environments. The context-aware systems (CAS) can
comprehensively interpret the surgical state, enable real-time warning, and
support decision-making, especially for novice surgeons. These systems can
automatically analyze surgical videos and perform indexing, documentation, and
post-operative report generation. The ever-increasing demand for such automatic
systems has sparked machine-learning-based approaches for surgical video
analysis. This thesis addresses the significant challenges in cataract surgery
video analysis to pave the way for building efficient context-aware systems.
The main contributions of this thesis are five folds: (1) This thesis
demonstrates that spatio-temporal localization of the relevant content can
considerably improve phase recognition accuracy. (2) This thesis proposes a
novel deep-learning-based framework for relevance-based compression to enable
real-time streaming and adaptive storage of cataract surgery videos. (3)
Several convolutional modules are proposed to boost the networks' semantic
interpretation performance in challenging conditions. These challenges include
blur and reflection distortion, transparency, deformability, color and texture
variation, blunt edges, and scale variation. (4) This thesis proposes and
evaluates the first framework for automatic irregularity detection in cataract
surgery videos. (5) To alleviate the requirement for manual pixel-based
annotations, this thesis proposes novel strategies for self-supervised
representation learning adapted to semantic segmentation.
