Search Results for author: Manxi Lin

Found 13 papers, 4 papers with code

Tri-modal Confluence with Temporal Dynamics for Scene Graph Generation in Operating Rooms

no code implementations14 Apr 2024 Diandian Guo, Manxi Lin, Jialun Pei, He Tang, Yueming Jin, Pheng-Ann Heng

A comprehensive understanding of surgical scenes allows for monitoring of the surgical process, reducing the occurrence of accidents and enhancing efficiency for medical professionals.

Graph Generation Scene Graph Generation

Shortcut Learning in Medical Image Segmentation

1 code implementation11 Mar 2024 Manxi Lin, Nina Weng, Kamil Mikolaj, Zahra Bashir, Morten Bo Søndergaard Svendsen, Martin Tolsgaard, Anders Nymark Christensen, Aasa Feragen

Shortcut learning is a phenomenon where machine learning models prioritize learning simple, potentially misleading cues from data that do not generalize well beyond the training set.

Image Classification Image Segmentation +3

S^2Former-OR: Single-Stage Bi-Modal Transformer for Scene Graph Generation in OR

1 code implementation22 Feb 2024 Jialun Pei, Diandian Guo, Jingyang Zhang, Manxi Lin, Yueming Jin, Pheng-Ann Heng

In this study, we introduce a novel single-stage bi-modal transformer framework for SGG in the OR, termed S^2Former-OR, aimed to complementally leverage multi-view 2D scenes and 3D point clouds for SGG in an end-to-end manner.

Graph Generation object-detection +3

An Automatic Guidance and Quality Assessment System for Doppler Imaging of Umbilical Artery

no code implementations11 Apr 2023 Chun Kit Wong, Manxi Lin, Alberto Raheli, Zahra Bashir, Morten Bo Søndergaard Svendsen, Martin Grønnebæk Tolsgaard, Aasa Feragen, Anders Nymark Christensen

Examination of the umbilical artery with Doppler ultrasonography is performed to investigate blood supply to the fetus through the umbilical cord, which is vital for the monitoring of fetal health.

Removing confounding information from fetal ultrasound images

no code implementations24 Mar 2023 Kamil Mikolaj, Manxi Lin, Zahra Bashir, Morten Bo Søndergaard Svendsen, Martin Tolsgaard, Anders Nymark, Aasa Feragen

In order to utilize the vast amounts of data available in these databases, we develop and validate a series of methods for minimizing the confounding effects of embedded text and calipers on deep learning algorithms designed for ultrasound, using standard plane classification as a test case.

Deep Learning

I saw, I conceived, I concluded: Progressive Concepts as Bottlenecks

no code implementations19 Nov 2022 Manxi Lin, Aasa Feragen, Zahra Bashir, Martin Grønnebæk Tolsgaard, Anders Nymark Christensen

Concept bottleneck models (CBMs) include a bottleneck of human-interpretable concepts providing explainability and intervention during inference by correcting the predicted, intermediate concepts.

Decision Making

DTU-Net: Learning Topological Similarity for Curvilinear Structure Segmentation

no code implementations23 May 2022 Manxi Lin, Zahra Bashir, Martin Grønnebæk Tolsgaard, Anders Nymark Christensen, Aasa Feragen

We conduct experiments on a challenging multi-class ultrasound scan segmentation dataset as well as a well-known retinal imaging dataset.

Segmentation Triplet

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