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Found 21 papers, 14 papers with code
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
Towards Unifying Anatomy Segmentation: Automated Generation of a Full-body CT Dataset via Knowledge Aggregation and Anatomical Guidelines
We examine its plausibility and usefulness using three complementary checks: Human expert evaluation which approved the dataset, a Deep Learning usefulness benchmark on the BTCV dataset in which we achieve 85% dice score without using its training dataset, and medical validity checks.
Why does my medical AI look at pictures of birds? Exploring the efficacy of transfer learning across domain boundaries
It is an open secret that ImageNet is treated as the panacea of pretraining.
CellViT: Vision Transformers for Precise Cell Segmentation and Classification
Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images are important clinical tasks and crucial for a wide range of applications.
Ranked #1 on
Cell Detection
on PanNuke
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.
Multimodal Interactive Lung Lesion Segmentation: A Framework for Annotating PET/CT Images based on Physiological and Anatomical Cues
Recently, deep learning enabled the accurate segmentation of various diseases in medical imaging.
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.
Valuing Vicinity: Memory attention framework for context-based semantic segmentation in histopathology
The segmentation of histopathological whole slide images into tumourous and non-tumourous types of tissue is a challenging task that requires the consideration of both local and global spatial contexts to classify tumourous regions precisely.
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.
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
Towards Automatic Parsing of Structured Visual Content through the Use of Synthetic Data
In this work, we propose a synthetic dataset, containing SVCs in the form of images as well as ground truths.
A Comparative Analysis of Decision-Level Fusion for Multimodal Driver Behaviour Understanding
Visual recognition inside the vehicle cabin leads to safer driving and more intuitive human-vehicle interaction but such systems face substantial obstacles as they need to capture different granularities of driver behaviour while dealing with highly limited body visibility and changing illumination.
Hierarchical Nearest Neighbor Graph Embedding for Efficient Dimensionality Reduction
Dimensionality reduction is crucial both for visualization and preprocessing high dimensional data for machine learning.
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.
Flying Guide Dog: Walkable Path Discovery for the Visually Impaired Utilizing Drones and Transformer-based Semantic Segmentation
By recognizing the color of pedestrian traffic lights, our prototype can help the user to cross a street safely.
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.
Pose2Drone: A Skeleton-Pose-based Framework for Human-Drone Interaction
Our framework provides the functionality to control the movement of the drone with simple arm gestures and to follow the user while keeping a safe distance.
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.
Prediction of low-keV monochromatic images from polyenergetic CT scans for improved automatic detection of pulmonary embolism
Detector-based spectral computed tomography is a recent dual-energy CT (DECT) technology that offers the possibility of obtaining spectral information.
Self-Guided Multiple Instance Learning for Weakly Supervised Disease Classification and Localization in Chest Radiographs
In this paper, we address the problem of weakly supervised identification and localization of abnormalities in chest radiographs.
Content and Colour Distillation for Learning Image Translations with the Spatial Profile Loss
In this paper, we propose a novel method of computing the loss directly between the source and target images that enable proper distillation of shape/content and colour/style.
