  Developing nations lack adequate number of hospitals with modern equipment
and skilled doctors. Hence, a significant proportion of these nations'
population, particularly in rural areas, is not able to avail specialized and
timely healthcare facilities. In recent years, deep learning (DL) models, a
class of artificial intelligence (AI) methods, have shown impressive results in
medical domain. These AI methods can provide immense support to developing
nations as affordable healthcare solutions. This work is focused on one such
application of blood cancer diagnosis. However, there are some challenges to DL
models in cancer research because of the unavailability of a large data for
adequate training and the difficulty of capturing heterogeneity in data at
different levels ranging from acquisition characteristics, session, to
subject-level (within subjects and across subjects). These challenges render DL
models prone to overfitting and hence, models lack generalization on
prospective subjects' data. In this work, we address these problems in the
application of B-cell Acute Lymphoblastic Leukemia (B-ALL) diagnosis using deep
learning. We propose heterogeneity loss that captures subject-level
heterogeneity, thereby, forcing the neural network to learn subject-independent
features. We also propose an unorthodox ensemble strategy that helps us in
providing improved classification over models trained on 7-folds giving a
weighted-$F_1$ score of 95.26% on unseen (test) subjects' data that are, so
far, the best results on the C-NMC 2019 dataset for B-ALL classification.
