Deep learning in histopathology: the path to the clinic
Abstract Machine learning techniques have great potential to improve medical diagnostics,
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …
Deep neural network models for computational histopathology: A survey
CL Srinidhi, O Ciga, AL Martel - Medical image analysis, 2021 - Elsevier
Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …
underlying mechanisms contributing to disease progression and patient survival outcomes …
Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
The medical imaging literature has witnessed remarkable progress in high-performing
segmentation models based on convolutional neural networks. Despite the new …
segmentation models based on convolutional neural networks. Despite the new …
Weakly supervised machine learning
Supervised learning aims to build a function or model that seeks as many mappings as
possible between the training data and outputs, where each training data will predict as a …
possible between the training data and outputs, where each training data will predict as a …
[HTML][HTML] Deep learning approaches to biomedical image segmentation
IRI Haque, J Neubert - Informatics in Medicine Unlocked, 2020 - Elsevier
The review covers automatic segmentation of images by means of deep learning
approaches in the area of medical imaging. Current developments in machine learning …
approaches in the area of medical imaging. Current developments in machine learning …
Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis
Cancer diagnosis, prognosis, mymargin and therapeutic response predictions are based on
morphological information from histology slides and molecular profiles from genomic data …
morphological information from histology slides and molecular profiles from genomic data …
Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Abstract Machine learning (ML) algorithms have made a tremendous impact in the field of
medical imaging. While medical imaging datasets have been growing in size, a challenge …
medical imaging. While medical imaging datasets have been growing in size, a challenge …
[HTML][HTML] Machine learning methods for histopathological image analysis
D Komura, S Ishikawa - Computational and structural biotechnology journal, 2018 - Elsevier
Abundant accumulation of digital histopathological images has led to the increased demand
for their analysis, such as computer-aided diagnosis using machine learning techniques …
for their analysis, such as computer-aided diagnosis using machine learning techniques …
Multiple instance learning for histopathological breast cancer image classification
PJ Sudharshan, C Petitjean, F Spanhol… - Expert Systems with …, 2019 - Elsevier
Histopathological images are the gold standard for breast cancer diagnosis. During
examination several dozens of them are acquired for a single patient. Conventional, image …
examination several dozens of them are acquired for a single patient. Conventional, image …
Constrained-CNN losses for weakly supervised segmentation
Weakly-supervised learning based on, eg, partially labelled images or image-tags, is
currently attracting significant attention in CNN segmentation as it can mitigate the need for …
currently attracting significant attention in CNN segmentation as it can mitigate the need for …