Whole slide image quality in digital pathology: review and perspectives
R Brixtel, S Bougleux, O Lézoray, Y Caillot… - IEEE …, 2022 - ieeexplore.ieee.org
With the advent of whole slide image (WSI) scanners, pathology is undergoing a digital
revolution. Simultaneously, with the development of image analysis algorithms based on …
revolution. Simultaneously, with the development of image analysis algorithms based on …
Hybrid models for classifying histological images: An association of deep features by transfer learning with ensemble classifier
The use of a convolutional neural network with transfer learning is a strategy that defines
high-level features, commonly explored to study patterns in medical images. These features …
high-level features, commonly explored to study patterns in medical images. These features …
Improved classification of colorectal polyps on histopathological images with ensemble learning and stain normalization
Abstract Background and Objective: Early detection of colon adenomatous polyps is critically
important because correct detection of it significantly reduces the potential of developing …
important because correct detection of it significantly reduces the potential of developing …
DL4ALL: Multi-task cross-dataset transfer learning for Acute Lymphoblastic Leukemia detection
Methods for the detection of Acute Lymphoblastic (or Lymphocytic) Leukemia (ALL) are
increasingly considering Deep Learning (DL) due to its high accuracy in several fields …
increasingly considering Deep Learning (DL) due to its high accuracy in several fields …
Classification of Multiple H&E Images via an Ensemble Computational Scheme
In this work, a computational scheme is proposed to identify the main combinations of
handcrafted descriptors and deep-learned features capable of classifying histological …
handcrafted descriptors and deep-learned features capable of classifying histological …
Siamese Content-based Search Engine for a More Transparent Skin and Breast Cancer Diagnosis through Histological Imaging
Z Tabatabaei, A Colomer, JAO Moll… - arXiv preprint arXiv …, 2024 - arxiv.org
Computer Aid Diagnosis (CAD) has developed digital pathology with Deep Learning (DL)-
based tools to assist pathologists in decision-making. Content-Based Histopathological …
based tools to assist pathologists in decision-making. Content-Based Histopathological …
RCKD: Response-Based Cross-Task Knowledge Distillation for Pathological Image Analysis
We propose a novel transfer learning framework for pathological image analysis, the
Response-based Cross-task Knowledge Distillation (RCKD), which improves the …
Response-based Cross-task Knowledge Distillation (RCKD), which improves the …
Ensemble Learning-Based Solutions: An Approach for Evaluating Multiple Features in the Context of H&E Histological Images
In this paper, we propose an approach based on ensemble learning to classify histology
tissues stained with hematoxylin and eosin. The proposal was applied to representative …
tissues stained with hematoxylin and eosin. The proposal was applied to representative …
GroupMixer: Patch-based Group Convolutional Neural Network for Breast Cancer Detection from Histopathological Images
A Modarres, EE Esfahani, M Bahrami - arXiv preprint arXiv:2311.09846, 2023 - arxiv.org
Diagnosis of breast cancer malignancy at the early stages is a crucial step for controlling its
side effects. Histopathological analysis provides a unique opportunity for malignant breast …
side effects. Histopathological analysis provides a unique opportunity for malignant breast …
ALL-IDB patches: Whole slide imaging for acute lymphoblastic leukemia detection using deep learning
The detection of Acute Lymphoblastic (or Lymphocytic) Leukemia (ALL) is being
increasingly performed using Deep Learning models (DL) that analyze each blood sample …
increasingly performed using Deep Learning models (DL) that analyze each blood sample …