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.

应用深度学习方法诊断白血病在发展中国家医疗中具有巨大的潜力，但由于数据稀缺和难以捕捉的异质性，深度学习模型需要应对过拟合的风险，本研究提出了一种异质性损失和非正统集成策略，取得了 C-NMC2019 数据集上 B-ALL 分类方面的最佳结果。

用异质损失函数处理癌症的不同受试者和同一受试者变异性

Heterogeneity Loss to Handle Intersubject and Intrasubject Variability  in Cancer

The lack of comprehensive, high-quality health data in developing nations
creates a roadblock for combating the impacts of disease. One key challenge is
understanding the health information needs of people in these nations. Without
understanding people's everyday needs, concerns, and misconceptions, health
organizations and policymakers lack the ability to effectively target education
and programming efforts. In this paper, we propose a bottom-up approach that
uses search data from individuals to uncover and gain insight into health
information needs in Africa. We analyze Bing searches related to HIV/AIDS,
malaria, and tuberculosis from all 54 African nations. For each disease, we
automatically derive a set of common search themes or topics, revealing a
wide-spread interest in various types of information, including disease
symptoms, drugs, concerns about breastfeeding, as well as stigma, beliefs in
natural cures, and other topics that may be hard to uncover through traditional
surveys. We expose the different patterns that emerge in health information
needs by demographic groups (age and sex) and country. We also uncover
discrepancies in the quality of content returned by search engines to users by
topic. Combined, our results suggest that search data can help illuminate
health information needs in Africa and inform discussions on health policy and
targeted education efforts both on- and offline.

使用搜索数据研究发展中国家健康信息需求，结果表明这种方法可以帮助制定和完善医疗政策和在线、离线教育计划，同时对于调查难以发现的相关问题，比如乳汁喂养，社会歧视等更有针对性。