Non-relevant segment recognition via hard example mining under sparsely distributed events.

We propose on/offline hard example mining (HEM) techniques to alleviate the degradation of the generalization performance in the sparse distribution of events in non-relevant segment (NRS) recognition and to examine their utility for long-duration surgery. Through on/offline HEM, higher recognition performance can be achieved by extracting hard examples that help train NRS events, for a given training dataset. Furthermore, we provide two performance measurement metrics to quantitatively evaluate NRS recognition in the clinical field.

Deep learning-based surgical phase recognition in laparoscopic cholecystectomy.

Backgrounds/aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases.

Molecular and cellular basis of sodium sensing in labellum.

Appropriate ingestion of salt is essential for physiological processes such as ionic homeostasis and neuronal activity. Generally, low concentrations of salt elicit attraction, while high concentrations elicit aversive responses. Here, we observed that sugar neurons in the L sensilla of the labellum cf.

Molecular and cellular organization of odorant binding protein genes in .

Chemosensation is important for the survival and reproduction of animals. The odorant binding proteins (OBPs) are thought to be involved in chemosensation together with chemosensory receptors. While OBPs were initially considered to deliver hydrophobic odorants to olfactory receptors in the aqueous lymph solution, recent studies suggest more complex roles in various organs.

Visual modalities-based multimodal fusion for surgical phase recognition.

Surgical workflow analysis is essential to help optimize surgery by encouraging efficient communication and the use of resources. However, the performance of phase recognition is limited by the use of information related to the presence of surgical instruments. To address the problem, we propose visual modality-based multimodal fusion for surgical phase recognition to overcome the limited diversity of information such as the presence of instruments.

PEg TRAnsfer Workflow recognition challenge report: Do multimodal data improve recognition?

Background And Objective: In order to be context-aware, computer-assisted surgical systems require accurate, real-time automatic surgical workflow recognition. In the past several years, surgical video has been the most commonly-used modality for surgical workflow recognition. But with the democratization of robot-assisted surgery, new modalities, such as kinematics, are now accessible.

Reducing redundancy in wireless capsule endoscopy videos.

We eliminate similar frames from a wireless capsule endoscopy video of the human intestines to maximize spatial coverage and minimize the redundancy in images. We combine an intensity correction method with a method based an optical flow and features to detect and reduce near-duplicate images acquired during the repetitive backward and forward egomotions due to peristalsis. In experiments, this technique reduced duplicate image of 52.