Skin cancer is a highly dangerous type of cancer that requires an accurate
diagnosis from experienced physicians. To help physicians diagnose skin cancer
more efficiently, a computer-aided diagnosis (CAD) system can be very helpful.
In this paper, we propose a novel model, which uses a novel attention mechanism
to pinpoint the differences in features across the spatial dimensions and
symmetry of the lesion, thereby focusing on the dissimilarities of various
classes based on symmetry, uniformity in texture and color, etc. Additionally,
to take into account the variations in the boundaries of the lesions for
different classes, we employ a gradient-based fusion of wavelet and soft
attention-aided features to extract boundary information of skin lesions. We
have tested our model on the multi-class and highly class-imbalanced dataset,
called HAM10000, and achieved promising results, with a 91.17\% F1-score and
90.75\% accuracy. The code is made available at:
this https URL

通过使用新颖的注意机制来聚焦于不同对称性、质地和颜色等特征的差异以及提取皮损边界信息的梯度融合小波和软注意辅助特征，我们的模型在多类别且高度不平衡的数据集 HAM10000 上测试并取得了有希望的结果，F1 得分为 91.17%，准确率为 90.75%。

基于小波引导的注意力模块与基于梯度的特征融合的皮肤癌分类

A Wavelet Guided Attention Module for Skin Cancer Classification with  Gradient-based Feature Fusion

This study aims to establish a computer-aided diagnosis system for
endobronchial ultrasound (EBUS) surgery to assist physicians in the preliminary
diagnosis of metastatic cancer. This involves arranging immediate examinations
for other sites of metastatic cancer after EBUS surgery, eliminating the need
to wait for reports, thereby shortening the waiting time by more than half and
enabling patients to detect other cancers earlier, allowing for early planning
and implementation of treatment plans. Unlike previous studies on cell image
classification, which have abundant datasets for training, this study must also
be able to make effective classifications despite the limited amount of case
data for lung metastatic cancer. In the realm of small data set classification
methods, Few-shot learning (FSL) has become mainstream in recent years. Through
its ability to train on small datasets and its strong generalization
capabilities, FSL shows potential in this task of lung metastatic cell image
classification. This study will adopt the approach of Few-shot learning,
referencing existing proposed models, and designing a model architecture for
classifying lung metastases cell images. Batch Spectral Regularization (BSR)
will be incorporated as a loss update parameter, and the Finetune method of PMF
will be modified. In terms of test results, the addition of BSR and the
modified Finetune method further increases the accuracy by 8.89% to 65.60%,
outperforming other FSL methods. This study confirms that FSL is superior to
supervised and transfer learning in classifying metastatic cancer and
demonstrates that using BSR as a loss function and modifying Finetune can
enhance the model's capabilities.

建立一种计算机辅助诊断系统，用于支持医生在内支气管超声（EBUS）手术中进行转移癌的初步诊断，通过安排 EBUS 手术后其他部位的立即检查，消除了等待报告的需要，将等待时间减半以上，并使患者能够更早地发现其他癌症，从而实现早期治疗计划的早期规划和实施。同时，通过引用现有的建议模型和设计模型架构来分类肺转移细胞图像，本研究证实了少样本学习在转移癌分类中的优越性，并表明使用 Batch Spectral Regularization（BSR）作为损失函数和修改 Finetune 能增强该模型的能力。

利用少样本学习通过内镜超声程序对细胞学影像进行初级肺癌和其他恶性肿瘤的分类

Using Few-Shot Learning to Classify Primary Lung Cancer and Other  Malignancy with Lung Metastasis in Cytological Imaging via Endobronchial  Ultrasound Procedures

Medical data collected for making a diagnostic decision are typically
multi-modal and provide complementary perspectives of a subject. A
computer-aided diagnosis system welcomes multi-modal inputs; however, how to
effectively fuse such multi-modal data is a challenging task and attracts a lot
of attention in the medical research field. In this paper, we propose a
transformer-based framework, called Alifuse, for aligning and fusing
multi-modal medical data. Specifically, we convert images and unstructured and
structured texts into vision and language tokens, and use intramodal and
intermodal attention mechanisms to learn holistic representations of all
imaging and non-imaging data for classification. We apply Alifuse to classify
Alzheimer's disease and obtain state-of-the-art performance on five public
datasets, by outperforming eight baselines. The source code will be available
online later.

提出了一种基于转换器的框架 Alifuse，用于对齐和融合多模态医学数据，应用于阿尔茨海默病的分类，并在五个公共数据集上取得了领先的性能。