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Found 11 papers, 5 papers with code
Improving Generalization Capability of Deep Learning-Based Nuclei Instance Segmentation by Non-deterministic Train Time and Deterministic Test Time Stain Normalization
With the advent of digital pathology and microscopic systems that can scan and save whole slide histological images automatically, there is a growing trend to use computerized methods to analyze acquired images.
NuInsSeg: A Fully Annotated Dataset for Nuclei Instance Segmentation in H&E-Stained Histological Images
In this work, we release one of the biggest fully manually annotated datasets of nuclei in Hematoxylin and Eosin (H&E)-stained histological images, called NuInsSeg.
Deep Neural Network Pruning for Nuclei Instance Segmentation in Hematoxylin & Eosin-Stained Histological Images
We investigate the impact of weight pruning on the performance of both branches separately and on the final nuclei instance segmentation result.
FUSeg: The Foot Ulcer Segmentation Challenge
Segmentation of wound boundaries in images is a key component of the care and diagnosis protocol since it is important to estimate the area of the wound and provide quantitative measurement for the treatment.
Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional Neural Networks
Foot ulcer is a common complication of diabetes mellitus and, associated with substantial morbidity and mortality, remains a major risk factor for lower leg amputations.
Improving Mask R-CNN for Nuclei Instance Segmentation in Hematoxylin & Eosin-Stained Histological Images
Recently, instance-aware segmentation methods such as Mask R-CNN have been proposed to enable unified instance detection and segmentation, even in overlapping cases.
CryoNuSeg: A Dataset for Nuclei Instance Segmentation of Cryosectioned H&E-Stained Histological Images
Analysis of FS-derived H&E stained images can be more challenging as rapid preparation, staining, and scanning of FS sections may lead to deterioration in image quality.
Pollen Grain Microscopic Image Classification Using an Ensemble of Fine-Tuned Deep Convolutional Neural Networks
Our proposed method is shown to yield excellent classification performance, obtaining an accuracy of of 94. 48% and a weighted F1-score of 94. 54% on the ICPR 2020 Pollen Grain Classification Challenge training dataset based on five-fold cross-validation.
The Effects of Skin Lesion Segmentation on the Performance of Dermatoscopic Image Classification
In this study, we explicitly investigated the impact of using skin lesion segmentation masks on the performance of dermatoscopic image classification.
Investigating and Exploiting Image Resolution for Transfer Learning-based Skin Lesion Classification
Our results show that using very small images (of size 64x64 pixels) degrades the classification performance, while images of size 128x128 pixels and above support good performance with larger image sizes leading to slightly improved classification.
Skin Lesion Classification Using Hybrid Deep Neural Networks
In this work, we propose a fully automatic computerised method for skin lesion classification which employs optimised deep features from a number of well-established CNNs and from different abstraction levels.
