Papers Histopathological Segmentation
“Histopathological Segmentation” 태그가 달린 논문 4편 · 필터 해제
GenSelfDiff-HIS: Generative Self-Supervision Using Diffusion for Histopathological Image Segmentation
Histopathological image segmentation is a laborious and time-intensive task, often requiring analysis from experienced pathologists for accurate examinations. To reduce this burden, supervised machine-learning approaches…
Histopathological SegmentationImage SegmentationImage-to-Image TranslationSegmentation+2Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualization
Deep learning methods have emerged as powerful tools for analyzing histopathological images, but current methods are often specialized for specific domains and software environments, and few open-source options exist for…
Deep LearningHistopathological Image ClassificationHistopathological SegmentationImage Generation+4MRL: Learning to Mix with Attention and Convolutions
In this paper, we present a new neural architectural block for the vision domain, named Mixing Regionally and Locally (MRL), developed with the aim of effectively and efficiently mixing the provided input features. We bi…
Histopathological SegmentationInductive BiasMulti-tissue Nucleus Segmentationobject-detection+1Weakly supervised multiple instance learning histopathological tumor segmentation
Histopathological image segmentation is a challenging and important topic in medical imaging with tremendous potential impact in clinical practice. State of the art methods rely on hand-crafted annotations which hinder c…
Histopathological SegmentationImage SegmentationMultiple Instance LearningSegmentation+4