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 …
Medical image analysis based on deep learning approach
M Puttagunta, S Ravi - Multimedia tools and applications, 2021 - Springer
Medical imaging plays a significant role in different clinical applications such as medical
procedures used for early detection, monitoring, diagnosis, and treatment evaluation of …
procedures used for early detection, monitoring, diagnosis, and treatment evaluation of …
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 …
Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review
Abstract Transparency in Machine Learning (ML), often also referred to as interpretability or
explainability, attempts to reveal the working mechanisms of complex models. From a …
explainability, attempts to reveal the working mechanisms of complex models. From a …
A comprehensive review of deep learning in colon cancer
Deep learning has emerged as a leading machine learning tool in object detection and has
attracted attention with its achievements in progressing medical image analysis …
attracted attention with its achievements in progressing medical image analysis …
Deep learning for whole slide image analysis: an overview
The widespread adoption of whole slide imaging has increased the demand for effective
and efficient gigapixel image analysis. Deep learning is at the forefront of computer vision …
and efficient gigapixel image analysis. Deep learning is at the forefront of computer vision …
IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach
In recent years, colorectal cancer has become one of the most significant diseases that
endanger human health. Deep learning methods are increasingly important for the …
endanger human health. Deep learning methods are increasingly important for the …
Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images
Abstract Machine-assisted pathological recognition has been focused on supervised
learning (SL) that suffers from a significant annotation bottleneck. We propose a semi …
learning (SL) that suffers from a significant annotation bottleneck. We propose a semi …
Deep learning on histopathological images for colorectal cancer diagnosis: a systematic review
Colorectal cancer (CRC) is the second most common cancer in women and the third most
common in men, with an increasing incidence. Pathology diagnosis complemented with …
common in men, with an increasing incidence. Pathology diagnosis complemented with …