Abnormal growth of cells in the brain and its surrounding tissues is known as
a brain tumor. There are two types, one is benign (non-cancerous) and another
is malignant (cancerous) which may cause death. The radiologists' ability to
diagnose malignancies is greatly aided by magnetic resonance imaging (MRI).
Brain cancer diagnosis has been considerably expedited by the field of
computer-assisted diagnostics, especially in machine learning and deep
learning. In our study, we categorize three different kinds of brain tumors
using four transfer learning techniques. Our models were tested on a benchmark
dataset of $3064$ MRI pictures representing three different forms of brain
cancer. Notably, ResNet-50 outperformed other models with a remarkable accuracy
of $99.06\%$. We stress the significance of a balanced dataset for improving
accuracy without the use of augmentation methods. Additionally, we
experimentally demonstrate our method and compare with other classification
algorithms on the CE-MRI dataset using evaluations like F1-score, AUC,
precision and recall.

利用四种迁移学习技术对三种不同类型的脑肿瘤进行分类，在基准数据集中，ResNet-50 模型以 99.06% 的显著准确率优于其他模型。我们强调，在不使用数据增强方法的情况下，通过保持数据集平衡以提高准确度的重要性，并通过 F1 分数、AUC、精确度和召回率等评估指标实验性地验证了我们的方法并与其他分类算法进行比较。

脑瘤分类的迁移学习的比较评估使用 MRI

Comparative Evaluation of Transfer Learning for Classification of Brain  Tumor Using MRI

Cancerous skin lesions are one of the most common malignancies detected in
humans, and if not detected at an early stage, they can lead to death.
Therefore, it is crucial to have access to accurate results early on to
optimize the chances of survival. Unfortunately, accurate results are typically
obtained by highly trained dermatologists, who may not be accessible to many
people, particularly in low-income and middle-income countries. Artificial
Intelligence (AI) appears to be a potential solution to this problem, as it has
proven to provide equal or even better diagnoses than healthcare professionals.
This project aims to address the issue by collecting state-of-the-art
techniques for image classification from various fields and implementing them.
Some of these techniques include mixup, presizing, and test-time augmentation,
among others. Three architectures were used for the implementation:
DenseNet121, VGG16 with batch normalization, and ResNet50. The models were
designed with two main purposes. First, to classify images into seven
categories, including melanocytic nevus, melanoma, benign keratosis-like
lesions, basal cell carcinoma, actinic keratoses and intraepithelial carcinoma,
vascular lesions, and dermatofibroma. Second, to classify images into benign or
malignant. The models were trained using a dataset of 8012 images, and their
performance was evaluated using 2003 images. It's worth noting that this model
is trained end-to-end, directly from the image to the labels, without the need
for handcrafted feature extraction.

通过人工智能和图像分类技术，将肿瘤性皮肤病变进行分类识别为恶性或良性，以实现早期有效诊断。

卷积神经网络用于皮肤病变诊断

Skin Lesion Diagnosis Using Convolutional Neural Networks

Discriminating lung nodules as malignant or benign is still an underlying
challenge. To address this challenge, radiologists need computer aided
diagnosis (CAD) systems which can assist in learning discriminative imaging
features corresponding to malignant and benign nodules. However, learning
highly discriminative imaging features is an open problem. In this paper, our
aim is to learn the most discriminative features pertaining to lung nodules by
using an adversarial learning methodology. Specifically, we propose to use
unsupervised learning with Deep Convolutional-Generative Adversarial Networks
(DC-GANs) to generate lung nodule samples realistically. We hypothesize that
imaging features of lung nodules will be discriminative if it is hard to
differentiate them (fake) from real (true) nodules. To test this hypothesis, we
present Visual Turing tests to two radiologists in order to evaluate the
quality of the generated (fake) nodules. Extensive comparisons are performed in
discerning real, generated, benign, and malignant nodules. This experimental
set up allows us to validate the overall quality of the generated nodules,
which can then be used to (1) improve diagnostic decisions by mining highly
discriminative imaging features, (2) train radiologists for educational
purposes, and (3) generate realistic samples to train deep networks with big
data.

本文提出使用 Deep Convolutional-Generative Adversarial Networks 的无监督学习方法来生成肺部结节样本，通过视觉图灵测试验证生成样本的质量，以此来学习最具有鉴别能力的影像特征，从而应用于医学诊断和训练深度网络。

如何用生成对抗网络愚弄放射科医生？肺癌诊断的视觉图灵测试

How to Fool Radiologists with Generative Adversarial Networks? A Visual  Turing Test for Lung Cancer Diagnosis

We present a deep learning framework for computer-aided lung cancer
diagnosis. Our multi-stage framework detects nodules in 3D lung CAT scans,
determines if each nodule is malignant, and finally assigns a cancer
probability based on these results. We discuss the challenges and advantages of
our framework. In the Kaggle Data Science Bowl 2017, our framework ranked 41st
out of 1972 teams.

本文针对肺癌的计算机辅助诊断问题，提出了一个多阶段的深度学习框架，通过 3D 肺部 CAT 扫描检测肺结节，判断恶性程度并计算癌症概率，参加 Kaggle Data Science Bowl 2017 竞赛并取得了第 41 名的好成绩。