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Found 15 papers, 5 papers with code
Siamese Content-based Search Engine for a More Transparent Skin and Breast Cancer Diagnosis through Histological Imaging
The Breast-twins model achieves 70% of the F1score at the top first, which exceeds the other state-of-the-art methods at a higher amount of K such as 5 and 400.
Attention to detail: inter-resolution knowledge distillation
The development of computer vision solutions for gigapixel images in digital pathology is hampered by significant computational limitations due to the large size of whole slide images.
A Spitzoid Tumor dataset with clinical metadata and Whole Slide Images for Deep Learning models
Spitzoid tumors (ST) are a group of melanocytic tumors of high diagnostic complexity.
WWFedCBMIR: World-Wide Federated Content-Based Medical Image Retrieval
The study shows that the FedCBMIR method increases the F1-Score (F1S) of each client to 98%, 96%, 94%, and 97% in the BreaKHis experiment with a generalized model of four magnifications and does so in 6. 30 hours less time than total local training.
A self-training framework for glaucoma grading in OCT B-scans
Particularly, the proposed two-step learning methodology resorts to pseudo-labels generated during the first step to augment the training dataset on the target domain, which is then used to train the final target model.
Circumpapillary OCT-Focused Hybrid Learning for Glaucoma Grading Using Tailored Prototypical Neural Networks
Glaucoma is one of the leading causes of blindness worldwide and Optical Coherence Tomography (OCT) is the quintessential imaging technique for its detection.
A Novel Self-Learning Framework for Bladder Cancer Grading Using Histopathological Images
Recently, bladder cancer has been significantly increased in terms of incidence and mortality.
Going Deeper through the Gleason Scoring Scale: An Automatic end-to-end System for Histology Prostate Grading and Cribriform Pattern Detection
The objective of the work presented in this paper is to develop a deep-learning-based system able to support pathologists in the daily analysis of prostate biopsies.
WeGleNet: A Weakly-Supervised Convolutional Neural Network for the Semantic Segmentation of Gleason Grades in Prostate Histology Images
Regarding the estimation of the core-level Gleason score, we obtained a k of 0. 76 and 0. 67 between the model and two different pathologists.
Self-learning for weakly supervised Gleason grading of local patterns
Particularly, the proposed model brings an average improvement on the Cohen's quadratic kappa (k) score of nearly 18% compared to full-supervision for the patch-level Gleason grading task.
Prostate Gland Segmentation in Histology Images via Residual and Multi-Resolution U-Net
Prostate cancer is one of the most prevalent cancers worldwide.
An Attention-based Weakly Supervised framework for Spitzoid Melanocytic Lesion Diagnosis in WSI
Nevertheless, no automatic CAD systems have yet been proposed for the analysis of spitzoid lesions.
Analysis of Hand-Crafted and Automatic-Learned Features for Glaucoma Detection Through Raw Circmpapillary OCT Images
Taking into account that glaucoma is the leading cause of blindness worldwide, we propose in this paper three different learning methodologies for glaucoma detection in order to elucidate that traditional machine-learning techniques could outperform deep-learning algorithms, especially when the image data set is small.
Glaucoma Detection From Raw Circumapillary OCT Images Using Fully Convolutional Neural Networks
Nowadays, glaucoma is the leading cause of blindness worldwide.
Gleason Grading of Histology Prostate Images through Semantic Segmentation via Residual U-Net
Worldwide, prostate cancer is one of the main cancers affecting men.
