Search Results for author:
Found 11 papers, 3 papers with code
NMGrad: Advancing Histopathological Bladder Cancer Grading with Weakly Supervised Deep Learning
To distinguish different levels of malignancy within specific regions of the slide, we include the origins of the tiles in our analysis.
Self-Contrastive Weakly Supervised Learning Framework for Prognostic Prediction Using Whole Slide Images
We present a pioneering investigation into the application of deep learning techniques to analyze histopathological images for addressing the substantial challenge of automated prognostic prediction.
Reconsidering evaluation practices in modular systems: On the propagation of errors in MRI prostate cancer detection
Magnetic resonance imaging has evolved as a key component for prostate cancer (PCa) detection, substantially increasing the radiologist workload.
Out-of-distribution multi-view auto-encoders for prostate cancer lesion detection
Our results show the potential of OOD approaches for PCa lesion detection based on MRI.
Leveraging multi-view data without annotations for prostate MRI segmentation: A contrastive approach
Further, our approach shows good external volumetric generalization in an in-house dataset when tested with multi-view data (2. 76+-1. 89% compared to 3. 92+-3. 31%, P=. 002), showing the feasibility of exploiting non-annotated multi-view data through contrastive learning whilst providing flexibility at deployment in the event of missing views.
Object Detection During Newborn Resuscitation Activities
Significance: This is the first step in a thorough analysis of newborn resuscitation episodes, which could provide important insight about the importance and effect of different newborn resuscitation activities
Activity Recognition From Newborn Resuscitation Videos
An important step is to generate timelines of relevant resuscitation activities, including ventilation, stimulation, suction, etc., during the resuscitation episodes.
Active Learning Based Domain Adaptation for Tissue Segmentation of Histopathological Images
Accurate segmentation of tissue in histopathological images can be very beneficial for defining regions of interest (ROI) for streamline of diagnostic and prognostic tasks.
3D Masked Modelling Advances Lesion Classification in Axial T2w Prostate MRI
Masked Image Modelling (MIM) has been shown to be an efficient self-supervised learning (SSL) pre-training paradigm when paired with transformer architectures and in the presence of a large amount of unlabelled natural images.
Nested Multiple Instance Learning with Attention Mechanisms
Strongly supervised learning requires detailed knowledge of truth labels at instance levels, and in many machine learning applications this is a major drawback.
Self-transfer learning via patches: A prostate cancer triage approach based on bi-parametric MRI
Nevertheless, they rely on large amounts of annotated data which is not common in the medical field.
