In this paper, we address the segmentation of tumor subtypes in whole slide
images (WSI) by utilizing incomplete label proportions. Specifically, we
utilize `partial' label proportions, which give the proportions among tumor
subtypes but do not give the proportion between tumor and non-tumor. Partial
label proportions are recorded as the standard diagnostic information by
pathologists, and we, therefore, want to use them for realizing the
segmentation model that can classify each WSI patch into one of the tumor
subtypes or non-tumor. We call this problem ``learning from partial label
proportions (LPLP)'' and formulate the problem as a weakly supervised learning
problem. Then, we propose an efficient algorithm for this challenging problem
by decomposing it into two weakly supervised learning subproblems: multiple
instance learning (MIL) and learning from label proportions (LLP). These
subproblems are optimized efficiently in the end-to-end manner. The
effectiveness of our algorithm is demonstrated through experiments conducted on
two WSI datasets.

本文通过利用不完全的标签比例解决了整个切片图像中肿瘤亚型的分割问题，提出了一个高效的算法将其分解为两个弱监督学习子问题：多实例学习和从标签比例中学习，从而有效地进行优化。该算法在两个整个切片图像数据集上经过实验证明了其有效性。

基于部分标签比例的全切片图像分割的学习

Learning from Partial Label Proportions for Whole Slide Image  Segmentation

Breast cancer is one of the leading causes of death for women worldwide.
Early screening is essential for early identification, but the chance of
survival declines as the cancer progresses into advanced stages. For this
study, the most recent BRACS dataset of histological (H\&E) stained images was
used to classify breast cancer tumours, which contains both the whole-slide
images (WSI) and region-of-interest (ROI) images, however, for our study we
have considered ROI images. We have experimented using different pre-trained
deep learning models, such as Xception, EfficientNet, ResNet50, and
InceptionResNet, pre-trained on the ImageNet weights. We pre-processed the
BRACS ROI along with image augmentation, upsampling, and dataset split
strategies. For the default dataset split, the best results were obtained by
ResNet50 achieving 66\% f1-score. For the custom dataset split, the best
results were obtained by performing upsampling and image augmentation which
results in 96.2\% f1-score. Our second approach also reduced the number of
false positive and false negative classifications to less than 3\% for each
class. We believe that our study significantly impacts the early diagnosis and
identification of breast cancer tumors and their subtypes, especially atypical
and malignant tumors, thus improving patient outcomes and reducing patient
mortality rates. Overall, this study has primarily focused on identifying seven
(7) breast cancer tumor subtypes, and we believe that the experimental models
can be fine-tuned further to generalize over previous breast cancer histology
datasets as well.

通过使用最新的 BRACS 组织学（H＆E 染色）图像数据集中的感兴趣区域（ROI）图像，我们使用不同的预训练深度学习模型，在处理图像时应用了数据增强、上采样和数据集拆分策略，最终达到了 96.2％的 f1-score，显著提高了乳腺癌肿瘤的早期诊断和鉴别，尤其是不典型和恶性肿瘤，从而改善患者预后并降低患者死亡率。