ALL Challenge dataset of ISBI 2019 (C-NMC 2019)

Acute lymphoblastic leukemia (ALL) constitutes approximately 25 of the pediatric cancers. In general, the task of identifying immature leukemic blasts from normal cells under the microscope is challenging because morphologically the images of the two cells appear similar. In this paper, we propose a deep learning framework for classifying immature leukemic blasts and normal cells. The proposed model combines the Discrete Cosine Transform (DCT) domain features extracted via CNN with the Optical Density (OD) space features to build a robust classifier. Elaborate experiments have been conducted to validate the proposed LeukoNet classifier.急性淋巴细胞白血病（Acute lymphoblastic leukemia, ALL）约占儿童癌症的25%。通常而言，在显微镜下区分未成熟白血病母细胞与正常细胞极具挑战性，二者的形态学图像外观高度相似。本文提出一种用于未成熟白血病母细胞与正常细胞分类的深度学习框架：所提模型将通过卷积神经网络（Convolutional Neural Network, CNN）提取的离散余弦变换（Discrete Cosine Transform, DCT）域特征，与光密度（Optical Density, OD）空间特征相结合，以构建鲁棒分类器。本文通过精心设计的实验对所提出的LeukoNet分类器进行了充分验证。

搜集汇总

背景与挑战

背景概述

该数据集是ISBI 2019 ALL Challenge（C-NMC 2019）的一部分，专注于急性淋巴细胞白血病（ALL）的细胞图像分类。数据集用于开发深度学习模型（如LeukoNet），以区分未成熟白血病母细胞和正常细胞，结合了离散余弦变换和光学密度特征来提高分类准确性。其特点在于解决显微镜图像中细胞形态相似性带来的挑战，支持儿科癌症诊断研究。