Search Results for author:
Found 13 papers, 8 papers with code
Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling
Results: Our resulting segmentation models demonstrated remarkable performance on CXR, with a high average model-annotator agreement between two radiologists with mIoU scores of 0. 93 and 0. 85 for frontal and lateral anatomy, while inter-annotator agreement remained at 0. 95 and 0. 83 mIoU.
360BEV: Panoramic Semantic Mapping for Indoor Bird's-Eye View
Seeing only a tiny part of the whole is not knowing the full circumstance.
Mirror U-Net: Marrying Multimodal Fission with Multi-task Learning for Semantic Segmentation in Medical Imaging
Positron Emission Tomography (PET) and Computer Tomography (CT) are routinely used together to detect tumors.
Delivering Arbitrary-Modal Semantic Segmentation
To make this possible, we present the arbitrary cross-modal segmentation model CMNeXt.
Ranked #1 on
Semantic Segmentation
on DSEC
Decoupled Semantic Prototypes Enable Learning From Diverse Annotation Types for Semi-Weakly Segmentation in Expert-Driven Domains
A vast amount of images and pixel-wise annotations allowed our community to build scalable segmentation solutions for natural domains.
Detailed Annotations of Chest X-Rays via CT Projection for Report Understanding
To exploit anatomical structures in this scenario, we present a sophisticated automatic pipeline to gather and integrate human bodily structures from computed tomography datasets, which we incorporate in our PAXRay: A Projected dataset for the segmentation of Anatomical structures in X-Ray data.
Behind Every Domain There is a Shift: Adapting Distortion-aware Vision Transformers for Panoramic Semantic Segmentation
In this paper, we address panoramic semantic segmentation which is under-explored due to two critical challenges: (1) image distortions and object deformations on panoramas; (2) lack of semantic annotations in the 360-degree imagery.
Ranked #1 on
Semantic Segmentation
on SynPASS
Breaking with Fixed Set Pathology Recognition through Report-Guided Contrastive Training
We show that despite using unstructured medical report supervision, we perform on par with direct label supervision through a sophisticated inference setting.
Ranked #1 on
Thoracic Disease Classification
on ChestX-ray14
Bending Reality: Distortion-aware Transformers for Adapting to Panoramic Semantic Segmentation
To get around this domain difference and bring together semantic annotations from pinhole- and 360-degree surround-visuals, we propose to learn object deformations and panoramic image distortions in the Deformable Patch Embedding (DPE) and Deformable MLP (DMLP) components which blend into our Transformer for PAnoramic Semantic Segmentation (Trans4PASS) model.
Ranked #2 on
Semantic Segmentation
on SynPASS
Reference-guided Pseudo-Label Generation for Medical Semantic Segmentation
Following this thought, we use a small number of labeled images as reference material and match pixels in an unlabeled image to the semantics of the best fitting pixel in a reference set.
Let's Play for Action: Recognizing Activities of Daily Living by Learning from Life Simulation Video Games
Recognizing Activities of Daily Living (ADL) is a vital process for intelligent assistive robots, but collecting large annotated datasets requires time-consuming temporal labeling and raises privacy concerns, e. g., if the data is collected in a real household.
Every Annotation Counts: Multi-label Deep Supervision for Medical Image Segmentation
Pixel-wise segmentation is one of the most data and annotation hungry tasks in our field.
Capturing Omni-Range Context for Omnidirectional Segmentation
Convolutional Networks (ConvNets) excel at semantic segmentation and have become a vital component for perception in autonomous driving.
Ranked #10 on
Semantic Segmentation
on DensePASS
(using extra training data)
