In this study, we proposed a model for skin disease classification using a
Bilinear Convolutional Neural Network (BCNN) with a Constrained Triplet Network
(CTN). BCNN can capture rich spatial interactions between features in image
data. This computes the outer product of feature vectors from two different
CNNs by a bilinear pooling. The resulting features encode second-order
statistics, enabling the network to capture more complex relationships between
different channels and spatial locations. The CTN employs the Triplet Loss
Function (TLF) by using a new loss layer that is added at the end of the
architecture called the Constrained Triplet Loss (CTL) layer. This is done to
obtain two significant learning objectives: inter-class categorization and
intra-class concentration with their deep features as often as possible, which
can be effective for skin disease classification. The proposed model is trained
to extract the intra-class features from a deep network and accordingly
increases the distance between these features, improving the model's
performance. The model achieved a mean accuracy of 93.72%.

通过使用受限三元组网络的双线性卷积神经网络模型，对皮肤疾病进行分类，实现了 93.72% 的平均准确率。

基于矩阵相似度的联合损失函数的双线性卷积神经网络用于皮肤疾病分类

Bilinear-Convolutional Neural Network Using a Matrix Similarity-based  Joint Loss Function for Skin Disease Classification

As skin cancer is one of the most frequent cancers globally, accurate,
non-invasive dermoscopy-based diagnosis becomes essential and promising. A task
of the Part 3 of the ISIC Skin Image Analysis Challenge at MICCAI 2018 is to
predict seven disease classes with skin lesion images, including melanoma
(MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic keratosis /
Bowen's disease (intraepithelial carcinoma) (AKIEC), benign keratosis (solar
lentigo / seborrheic keratosis / lichen planus-like keratosis) (BKL),
dermatofibroma (DF) and vascular lesion (VASC) as defined by the International
Dermatology Society.
In this work, we design the WonDerM pipeline, that resamples the preprocessed
skin lesion images, builds neural network architecture fine-tuned with
segmentation task data (the Part 1), and uses an ensemble method to classify
the seven skin diseases. Our model achieved an accuracy of 0.899 and 0.785 in
the validation set and test set, respectively.

该研究旨在通过深度学习和神经网络应用于皮肤科图像分析挑战解决皮肤病的预测和分类问题，并在验证数据集和测试数据集中分别实现了 0.899 和 0.785 的准确率。