Search Results for author:
Found 24 papers, 7 papers with code
Constrained unsupervised anomaly segmentation
In particular, the equality constraint on the attention maps in prior work is replaced by an inequality constraint, which allows more flexibility.
Looking at the whole picture: constrained unsupervised anomaly segmentation
In particular, the equality constraint on the attention maps in prior work is replaced by an inequality constraint, which allows more flexibility.
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.
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.
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.
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.
REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs
As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one.
Gleason Grading of Histology Prostate Images through Semantic Segmentation via Residual U-Net
Worldwide, prostate cancer is one of the main cancers affecting men.
Glaucoma Detection From Raw Circumapillary OCT Images Using Fully Convolutional Neural Networks
Nowadays, glaucoma is the leading cause of blindness worldwide.
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.
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.
Prostate Gland Segmentation in Histology Images via Residual and Multi-Resolution U-Net
Prostate cancer is one of the most prevalent cancers worldwide.
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.
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.
DCASE 2022: Comparative Analysis Of CNNs For Acoustic Scene Classification Under Low-Complexity Considerations
Due to the drift in this field of study, this task has two limitations in terms of model complexity.
Challenging mitosis detection algorithms: Global labels allow centroid localization
Mitotic activity is a crucial proliferation biomarker for the diagnosis and prognosis of different types of cancers.
Self-supervised learning of a tailored Convolutional Auto Encoder for histopathological prostate grading
In particular, a tailored Convolutional Auto Encoder (CAE) is trained to reconstruct 128x128x3 patches of prostate cancer Whole Slide Images (WSIs) as a pretext task.
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.
Towards More Transparent and Accurate Cancer Diagnosis with an Unsupervised CAE Approach
UCBMIR outperformed previous studies, achieving a top 5 recall of 99% and 80% on BreaKHis and SICAPv2, respectively, using the first evaluation technique.
HistoColAi: An Open-Source Web Platform for Collaborative Digital Histology Image Annotation with AI-Driven Predictive Integration
Digital pathology has become a standard in the pathology workflow due to its many benefits.
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.
Emotional Voice Messages (EMOVOME) database: emotion recognition in spontaneous voice messages
For speech, we used the standard eGeMAPS feature set and support vector machines, obtaining 49. 27% and 44. 71% unweighted accuracy for valence and arousal respectively.
Speech emotion recognition from voice messages recorded in the wild
The pre-trained Unispeech-L model and its combination with eGeMAPS achieved the highest results, with 61. 64% and 55. 57% Unweighted Accuracy (UA) for 3-class valence and arousal prediction respectively, a 10% improvement over baseline models.
