Papers Multi-tissue Nucleus Segmentation
“Multi-tissue Nucleus Segmentation” 태그가 달린 논문 14편 · 필터 해제
MRL: 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+1SONNET: A Self-Guided Ordinal Regression Neural Network for Segmentation and Classification of Nuclei in Large-Scale Multi-Tissue Histology Images
Automated nuclei segmentation and classification are the keys to analyze and understand the cellular characteristics and functionality, supporting computer-aided digital pathology in disease diagnosis. However, the task …
ClassificationMulti-tissue Nucleus SegmentationNuclear SegmentationNuclei Classification+2PointNu-Net: Keypoint-assisted Convolutional Neural Network for Simultaneous Multi-tissue Histology Nuclei Segmentation and Classification
Automatic nuclei segmentation and classification play a vital role in digital pathology. However, previous works are mostly built on data with limited diversity and small sizes, making the results questionable or mislead…
ClassificationDiversityInstance SegmentationKeypoint Estimation+3Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images
Histology images are inherently symmetric under rotation, where each orientation is equally as likely to appear. However, this rotational symmetry is not widely utilised as prior knowledge in modern Convolutional Neural …
Breast Tumour ClassificationColorectal Gland Segmentation:Multi-tissue Nucleus SegmentationNuclear Segmentation+1Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis
Rotation-invariance is a desired property of machine-learning models for medical image analysis and in particular for computational pathology applications. We propose a framework to encode the geometric structure of the …
BIG-bench Machine LearningBreast Tumour ClassificationColorectal Gland Segmentation:Data Augmentation+4CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation
Accurate segmenting nuclei instances is a crucial step in computer-aided image analysis to extract rich features for cellular estimation and following diagnosis as well as treatment. While it still remains challenging be…
Instance SegmentationMulti-tissue Nucleus SegmentationSegmentationSemantic SegmentationHoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images
Nuclear segmentation and classification within Haematoxylin & Eosin stained histology images is a fundamental prerequisite in the digital pathology work-flow. The development of automated methods for nuclear segmentation…
ClassificationGeneral ClassificationMulti-tissue Nucleus SegmentationNuclear Segmentation+1Micro-Net: A unified model for segmentation of various objects in microscopy images
Object segmentation and structure localization are important steps in automated image analysis pipelines for microscopy images. We present a convolution neural network (CNN) based deep learning architecture for segmentat…
Deep LearningMulti-tissue Nucleus SegmentationSegmentationSemantic SegmentationLearning Steerable Filters for Rotation Equivariant CNNs
In many machine learning tasks it is desirable that a model's prediction transforms in an equivariant way under transformations of its input. Convolutional neural networks (CNNs) implement translational equivariance by c…
Breast Tumour ClassificationColorectal Gland Segmentation:Multi-tissue Nucleus SegmentationRotated MNISTMask R-CNN
We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask fo…
3D Instance SegmentationHuman Part SegmentationInstance SegmentationKeypoint Detection+14Rotation equivariant vector field networks
In many computer vision tasks, we expect a particular behavior of the output with respect to rotations of the input image. If this relationship is explicitly encoded, instead of treated as any other variation, the comple…
Breast Tumour ClassificationColorectal Gland Segmentation:image-classificationImage Classification+4Group Equivariant Convolutional Networks
We introduce Group equivariant Convolutional Neural Networks (G-CNNs), a natural generalization of convolutional neural networks that reduces sample complexity by exploiting symmetries. G-CNNs use G-convolutions, a new t…
Breast Tumour ClassificationColorectal Gland Segmentation:Multi-tissue Nucleus SegmentationRotated MNISTU-Net: Convolutional Networks for Biomedical Image Segmentation
There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmenta…
Cell SegmentationCell TrackingColorectal Gland Segmentation:Crack Segmentation+14Fully Convolutional Networks for Semantic Segmentation
Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segme…
Crack SegmentationMultispectral Object DetectionMulti-tissue Nucleus SegmentationSegmentation+2