Papers Breast Tumour Classification
“Breast Tumour Classification” 태그가 달린 논문 13편 · 필터 해제
Attention-Map Augmentation for Hypercomplex Breast Cancer Classification
Breast cancer is the most widespread neoplasm among women and early detection of this disease is critical. Deep learning techniques have become of great interest to improve diagnostic performance. However, distinguishing…
Breast Cancer Histology Image ClassificationBreast Tumour ClassificationCancer ClassificationClassification+2Virchow: A Million-Slide Digital Pathology Foundation Model
The use of artificial intelligence to enable precision medicine and decision support systems through the analysis of pathology images has the potential to revolutionize the diagnosis and treatment of cancer. Such applica…
Breast Tumour ClassificationCancer ClassificationmodelSelf-Supervised Learning+1Multi-View Hypercomplex Learning for Breast Cancer Screening
Traditionally, deep learning methods for breast cancer classification perform a single-view analysis. However, radiologists simultaneously analyze all four views that compose a mammography exam, owing to the correlations…
Breast Tumour ClassificationCancer ClassificationCancer-no cancer per breast classificationClassification+2Meta-repository of screening mammography classifiers
Artificial intelligence (AI) is showing promise in improving clinical diagnosis. In breast cancer screening, recent studies show that AI has the potential to improve early cancer diagnosis and reduce unnecessary workup. …
Breast Cancer DetectionBreast Tumour ClassificationMedical Image AnalysisAn End-to-End Breast Tumour Classification Model Using Context-Based Patch Modelling- A BiLSTM Approach for Image Classification
Researchers working on computational analysis of Whole Slide Images (WSIs) in histopathology have primarily resorted to patch-based modelling due to large resolution of each WSI. The large resolution makes WSIs infeasibl…
Breast Tumour ClassificationClassificationimage-classificationImage Classification+2BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis
This paper describes the field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening. The main contributions described here are threefold: 1) The design of an adv…
3D Medical Imaging SegmentationAutomatic Machine Learning Model SelectionBreast Cancer DetectionBreast Mass Segmentation In Whole Mammograms+6Dense 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+4Rotation Equivariant CNNs for Digital Pathology
We propose a new model for digital pathology segmentation, based on the observation that histopathology images are inherently symmetric under rotation and reflection. Utilizing recent findings on rotation equivariant CNN…
BIG-bench Machine LearningBreast Tumour ClassificationLearning 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 MNISTRotation 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+4Densely Connected Convolutional Networks
Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In…
Breast Tumour ClassificationClassificationCrowd CountingImage Classification+7Group 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 MNIST