One label is all you need: Interpretable AI-enhanced histopathology for oncology
Artificial Intelligence (AI)-enhanced histopathology presents unprecedented opportunities to
benefit oncology through interpretable methods that require only one overall label per …
benefit oncology through interpretable methods that require only one overall label per …
[HTML][HTML] Model compression techniques in biometrics applications: A survey
The development of deep learning algorithms has extensively empowered humanity's task
automatization capacity. However, the huge improvement in the performance of these …
automatization capacity. However, the huge improvement in the performance of these …
[HTML][HTML] Annotating for artificial intelligence applications in digital pathology: a practical guide for pathologists and researchers
Training machine learning models for artificial intelligence (AI) applications in pathology
often requires extensive annotation by human experts, but there is little guidance on the …
often requires extensive annotation by human experts, but there is little guidance on the …
Contrastive multiple instance learning: An unsupervised framework for learning slide-level representations of whole slide histopathology images without labels
Simple Summary Recent AI methods in the automated analysis of histopathological imaging
data associated with cancer have trended towards less supervision by humans. Yet, there …
data associated with cancer have trended towards less supervision by humans. Yet, there …
Evaluating AI in medicine: a comparative analysis of expert and ChatGPT responses to colorectal cancer questions
W Peng, Y Feng, C Yao, S Zhang, H Zhuo, T Qiu… - Scientific Reports, 2024 - nature.com
Colorectal cancer (CRC) is a global health challenge, and patient education plays a crucial
role in its early detection and treatment. Despite progress in AI technology, as exemplified by …
role in its early detection and treatment. Despite progress in AI technology, as exemplified by …
A survey on cell nuclei instance segmentation and classification: Leveraging context and attention
Nuclear-derived morphological features and biomarkers provide relevant insights regarding
the tumour microenvironment, while also allowing diagnosis and prognosis in specific …
the tumour microenvironment, while also allowing diagnosis and prognosis in specific …
NSGA-II-DL: Metaheuristic optimal feature selection with Deep Learning Framework for HER2 classification in Breast Cancer
Immunohistochemistry (IHC) slides are graded for breast cancer based on visual markers
and morphological characteristics of stained membrane regions. The usage of whole slide …
and morphological characteristics of stained membrane regions. The usage of whole slide …
An interpretable machine learning system for colorectal cancer diagnosis from pathology slides
Considering the profound transformation affecting pathology practice, we aimed to develop
a scalable artificial intelligence (AI) system to diagnose colorectal cancer from whole-slide …
a scalable artificial intelligence (AI) system to diagnose colorectal cancer from whole-slide …
Adenoma dysplasia grading of colorectal polyps using fast fourier convolutional ResNet (FFC-ResNet)
MP Paing, C Pintavirooj - IEEE Access, 2023 - ieeexplore.ieee.org
Colorectal polyps are precursor lesions of colorectal cancer; hence, early detection and
dysplasia grading of polyps are essential for determining cancer risk, the possibility of …
dysplasia grading of polyps are essential for determining cancer risk, the possibility of …
Domain generalization in computational pathology: survey and guidelines
Deep learning models have exhibited exceptional effectiveness in Computational Pathology
(CPath) by tackling intricate tasks across an array of histology image analysis applications …
(CPath) by tackling intricate tasks across an array of histology image analysis applications …