1.5k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of BaselCH21 papers
- University of LausanneCH7 papers
- University of BernCH6 papers
- German Cancer Research CenterDE5 papers
- Heidelberg UniversityDE5 papers
- University Hospital of BernCH5 papers
- University of ZurichCH5 papers
- Centre National de la Recherche ScientifiqueFR4 papers
- ETH ZurichCH4 papers
- HES-SO University of Applied Sciences and Arts Western SwitzerlandCH4 papers
- Centre d'Imagerie BioMedicaleCH3 papers
- École Polytechnique Fédérale de LausanneCH3 papers
9 papers · 1 filter
OCELOT 2023: Cell Detection from Cell-Tissue Interaction Challenge
JaeWoong Shin, Jeongun Ryu, Aaron Valero Puche +21
Pathologists routinely alternate between different magnifications when examining Whole-Slide Images, allowing them to evaluate both broad tissue morphology and intricate cellular d…
A weakly-supervised deep learning model for fast localisation and delineation of the skeleton, internal organs, and spinal canal on Whole-Body Diffusion-Weighted MRI (WB-DWI)
A. Candito, A. Dragan, R. Holbrey +6
Background: Apparent Diffusion Coefficient (ADC) values and Total Diffusion Volume (TDV) from Whole-body diffusion-weighted MRI (WB-DWI) are recognized cancer imaging biomarkers. H…
PASSION for Dermatology: Bridging the Diversity Gap with Pigmented Skin Images from Sub-Saharan Africa
Philippe Gottfrois, Fabian Gröger, Faly Herizo Andriambololoniaina +13
Africa faces a huge shortage of dermatologists, with less than one per million people. This is in stark contrast to the high demand for dermatologic care, with 80% of the paediatri…
OMG-Net: A Deep Learning Framework Deploying Segment Anything to Detect Pan-Cancer Mitotic Figures from Haematoxylin and Eosin-Stained Slides
Zhuoyan Shen, Mikael Simard, Douglas Brand +14
Mitotic activity is an important feature for grading several cancer types. Counting mitotic figures (MFs) is a time-consuming, laborious task prone to inter-observer variation. Ina…
Surgical Text-to-Image Generation
Chinedu Innocent Nwoye, Rupak Bose, Kareem Elgohary +5
Acquiring surgical data for research and development is significantly hindered by high annotation costs and practical and ethical constraints. Utilizing synthetically generated ima…
Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentation
Nataliia Molchanova, Vatsal Raina, Andrey Malinin +7
This paper explores uncertainty quantification (UQ) as an indicator of the trustworthiness of automated deep-learning (DL) tools in the context of white matter lesion (WML) segment…
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Jianning Li, Zongwei Zhou, Jiancheng Yang +154
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…
Preserving Privacy in Surgical Video Analysis Using Artificial Intelligence: A Deep Learning Classifier to Identify Out-of-Body Scenes in Endoscopic Videos
Joël L. Lavanchy, Armine Vardazaryan, Pietro Mascagni +3
Objective: To develop and validate a deep learning model for the identification of out-of-body images in endoscopic videos. Background: Surgical video analysis facilitates educatio…
Spinal Cord Gray Matter-White Matter Segmentation on Magnetic Resonance AMIRA Images with MD-GRU
Antal Horvath, Charidimos Tsagkas, Simon Andermatt +3
The small butterfly shaped structure of spinal cord (SC) gray matter (GM) is challenging to image and to delinate from its surrounding white matter (WM). Segmenting GM is up to a p…