Transfer learning techniques for medical image analysis: A review
Medical imaging is a useful tool for disease detection and diagnostic imaging technology
has enabled early diagnosis of medical conditions. Manual image analysis methods are …
has enabled early diagnosis of medical conditions. Manual image analysis methods are …
[HTML][HTML] Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction
D Nam, J Chapiro, V Paradis, TP Seraphin, JN Kather - Jhep Reports, 2022 - Elsevier
Clinical routine in hepatology involves the diagnosis and treatment of a wide spectrum of
metabolic, infectious, autoimmune and neoplastic diseases. Clinicians integrate qualitative …
metabolic, infectious, autoimmune and neoplastic diseases. Clinicians integrate qualitative …
Deep learning in cancer diagnosis and prognosis prediction: a minireview on challenges, recent trends, and future directions
AB Tufail, YK Ma, MKA Kaabar… - … Methods in Medicine, 2021 - Wiley Online Library
Deep learning (DL) is a branch of machine learning and artificial intelligence that has been
applied to many areas in different domains such as health care and drug design. Cancer …
applied to many areas in different domains such as health care and drug design. Cancer …
[HTML][HTML] Artificial intelligence in gastroenterology: A state-of-the-art review
PT Kröner, MML Engels, BS Glicksberg… - World journal of …, 2021 - ncbi.nlm.nih.gov
The development of artificial intelligence (AI) has increased dramatically in the last 20 years,
with clinical applications progressively being explored for most of the medical specialties …
with clinical applications progressively being explored for most of the medical specialties …
A ViT-AMC network with adaptive model fusion and multiobjective optimization for interpretable laryngeal tumor grading from histopathological images
The tumor grading of laryngeal cancer pathological images needs to be accurate and
interpretable. The deep learning model based on the attention mechanism-integrated …
interpretable. The deep learning model based on the attention mechanism-integrated …
A survey on artificial intelligence in histopathology image analysis
The increasing adoption of the whole slide image (WSI) technology in histopathology has
dramatically transformed pathologists' workflow and allowed the use of computer systems in …
dramatically transformed pathologists' workflow and allowed the use of computer systems in …
[HTML][HTML] Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis
Artificial Intelligence (AI) has recently altered the landscape of cancer research and medical
oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep …
oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep …
Cuff-less blood pressure estimation from photoplethysmography via visibility graph and transfer learning
This paper presents a new solution that enables the use of transfer learning for cuff-less
blood pressure (BP) monitoring via short duration of photoplethysmogram (PPG). The …
blood pressure (BP) monitoring via short duration of photoplethysmogram (PPG). The …
[HTML][HTML] A DenseNet CNN-based liver lesion prediction and classification for future medical diagnosis
Liver disease diagnosis is a major medical challenge in developing nations. Every year
around 30 billion people face liver failure issues resulting in their death. The past liver …
around 30 billion people face liver failure issues resulting in their death. The past liver …
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 …