  Earlier diagnosis of Leukemia can save thousands of lives annually. The
prognosis of leukemia is challenging without the morphological information of
White Blood Cells (WBC) and relies on the accessibility of expensive
microscopes and the availability of hematologists to analyze Peripheral Blood
Samples (PBS). Deep Learning based methods can be employed to assist
hematologists. However, these algorithms require a large amount of labeled
data, which is not readily available. To overcome this limitation, we have
acquired a realistic, generalized, and large dataset. To collect this
comprehensive dataset for real-world applications, two microscopes from two
different cost spectrums (high-cost HCM and low-cost LCM) are used for dataset
capturing at three magnifications (100x, 40x, 10x) through different sensors
(high-end camera for HCM, middle-level camera for LCM and mobile-phone camera
for both). The high-sensor camera is 47 times more expensive than the
middle-level camera and HCM is 17 times more expensive than LCM. In this
collection, using HCM at high resolution (100x), experienced hematologists
annotated 10.3k WBC types (14) and artifacts, having 55k morphological labels
(Cell Size, Nuclear Chromatin, Nuclear Shape, etc.) from 2.4k images of several
PBS leukemia patients. Later on, these annotations are transferred to other 2
magnifications of HCM, and 3 magnifications of LCM, and on each camera captured
images. Along with the LeukemiaAttri dataset, we provide baselines over
multiple object detectors and Unsupervised Domain Adaptation (UDA) strategies,
along with morphological information-based attribute prediction. The dataset
will be publicly available after publication to facilitate the research in this
direction.
