Masked-Attention Transformers for Surgical Instrument Segmentation

Flashcards created from lecture notes on Masked-Attention Transformers for surgical instrument segmentation, covering key vocabulary and concepts.

Masked Attention Transformers (MATIS)

A two-stage, fully transformer-based method for surgical instrument segmentation using pixel-wise attention mechanisms.

Instrument Segmentation

The process of accurately identifying and segmenting surgical instruments in medical imaging.

Endovis 2017 and Endovis 2018

Standard public benchmarks used to validate the performance of surgical instrument segmentation methods.

Pixel-wise Attention Mechanisms

Techniques that allow models to focus on specific pixels in an image for more accurate segmentation tasks.

Temporal Consistency Module

A component that incorporates long-term video-level information to enhance mask classification and maintain recognition across frames.

Convolutional Neural Networks (CNNs)

A class of deep neural networks commonly used for analyzing visual imagery, often utilized in surgical instrument segmentation before transformer models.

Fully Convolutional Networks (FCNs)

A type of CNN architecture used for pixel-wise predictions and effective in semantic segmentation tasks.

Vision Transformers (ViTs)

A type of deep learning architecture that applies transformer models to vision tasks, demonstrating state-of-the-art performances.

Multi-scale Deformable Attention

A mechanism that allows models to adaptively focus on relevant portions of an image at different scales for improved segmentation.

Mean Intersection over Union (mIoU)

An evaluation metric used to measure the average accuracy of predicted segmentation masks compared to the ground truth.