Digital pathology has revolutionized cancer diagnosis by leveraging
Content-Based Medical Image Retrieval (CBMIR) for analyzing histopathological
Whole Slide Images (WSIs). CBMIR enables searching for similar content,
enhancing diagnostic reliability and accuracy. In 2020, breast and prostate
cancer constituted 11.7% and 14.1% of cases, respectively, as reported by the
Global Cancer Observatory (GCO). The proposed Unsupervised CBMIR (UCBMIR)
replicates the traditional cancer diagnosis workflow, offering a dependable
method to support pathologists in WSI-based diagnostic conclusions. This
approach alleviates pathologists' workload, potentially enhancing diagnostic
efficiency. To address the challenge of the lack of labeled histopathological
images in CBMIR, a customized unsupervised Convolutional Auto Encoder (CAE) was
developed, extracting 200 features per image for the search engine component.
UCBMIR was evaluated using widely-used numerical techniques in CBMIR, alongside
visual evaluation and comparison with a classifier. The validation involved
three distinct datasets, with an external evaluation demonstrating its
effectiveness. UCBMIR outperformed previous studies, achieving a top 5 recall
of 99% and 80% on BreaKHis and SICAPv2, respectively, using the first
evaluation technique. Precision rates of 91% and 70% were achieved for BreaKHis
and SICAPv2, respectively, using the second evaluation technique. Furthermore,
UCBMIR demonstrated the capability to identify various patterns in patches,
achieving an 81% accuracy in the top 5 when tested on an external image from
Arvaniti.

通过使用基于内容的医学图像检索 (CBMIR) 分析组织病理学切片图像，数字病理学通过提供一种强大的工具进而革命了肿瘤诊断；为了解决 CBMIR 中缺乏标注的组织病理学图像的问题，文中提出了一种无监督学习方法 (UCBMIR), 并采用基于卷积自编码器 (CAE) 的自定义方法提取特征，经过实验表明该方法具有较高的识别准确率。

采用无监督 CAE 方法实现更透明和准确的癌症诊断

Towards More Transparent and Accurate Cancer Diagnosis with an  Unsupervised CAE Approach

The paper proposes a Federated Content-Based Medical Image Retrieval
(FedCBMIR) platform that utilizes Federated Learning (FL) to address the
challenges of acquiring a diverse medical data set for training CBMIR models.
CBMIR assists pathologists in diagnosing breast cancer more rapidly by
identifying similar medical images and relevant patches in prior cases compared
to traditional cancer detection methods. However, CBMIR in histopathology
necessitates a pool of Whole Slide Images (WSIs) to train to extract an optimal
embedding vector that leverages search engine performance, which may not be
available in all centers. The strict regulations surrounding data sharing in
medical data sets also hinder research and model development, making it
difficult to collect a rich data set. The proposed FedCBMIR distributes the
model to collaborative centers for training without sharing the data set,
resulting in shorter training times than local training. FedCBMIR was evaluated
in two experiments with three scenarios on BreaKHis and Camelyon17 (CAM17). The
study shows that the FedCBMIR method increases the F1-Score (F1S) of each
client to 98%, 96%, 94%, and 97% in the BreaKHis experiment with a generalized
model of four magnifications and does so in 6.30 hours less time than total
local training. FedCBMIR also achieves 98% accuracy with CAM17 in 2.49 hours
less training time than local training, demonstrating that our FedCBMIR is both
fast and accurate for both pathologists and engineers. In addition, our
FedCBMIR provides similar images with higher magnification for non-developed
countries where participate in the worldwide FedCBMIR with developed countries
to facilitate mitosis measuring in breast cancer diagnosis. We evaluate this
scenario by scattering BreaKHis into four centers with different
magnifications.

该论文提出了一种利用联邦学习解决获取多样化医学数据集的挑战的联邦基于内容的医学图像检索平台（FedCBMIR），可加速乳腺癌诊断，并在 BreaKHis 和 Camelyon17（CAM17）进行了两个实验的评估，证明其快速准确，可在全球范围内帮助乳腺癌诊断。