Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospects

P Kumari, J Chauhan, A Bozorgpour, B Huang… - arxiv preprint arxiv …, 2023‏ - arxiv.org
Medical imaging analysis has witnessed remarkable advancements even surpassing
human-level performance in recent years, driven by the rapid development of advanced …

Domain generalization in computational pathology: Survey and guidelines

M Jahanifar, M Raza, K Xu, T Vuong… - arxiv preprint arxiv …, 2023‏ - arxiv.org
Deep learning models have exhibited exceptional effectiveness in Computational Pathology
(CPath) by tackling intricate tasks across an array of histology image analysis applications …

Continual learning in medical image analysis: A survey

X Wu, Z Xu, RK Tong - Computers in Biology and Medicine, 2024‏ - Elsevier
In the dynamic realm of practical clinical scenarios, Continual Learning (CL) has gained
increasing interest in medical image analysis due to its potential to address major …

DinoBloom: a foundation model for generalizable cell embeddings in hematology

V Koch, SJ Wagner, S Kazeminia, E Sancar… - … Conference on Medical …, 2024‏ - Springer
In hematology, computational models offer significant potential to improve diagnostic
accuracy, streamline workflows, and reduce the tedious work of analyzing single cells in …

Neural Cellular Automata for Lightweight, Robust and Explainable Classification of White Blood Cell Images

M Deutges, A Sadafi, N Navab, C Marr - International Conference on …, 2024‏ - Springer
Diagnosis of hematological malignancies depends on accurate identification of white blood
cells in peripheral blood smears. Deep learning techniques are emerging as a viable …

Automated Detection of White Blood Cells and Platelets From Microscopic Images using Deep Learning Models

XMT Nguyen, VQ Nguyen - 2024‏ - elib.vku.udn.vn
This study focuses on the detection of white blood cells (WBCs) and platelets using deep
learning (DL) models on microscopic image data. The accurate identification and …