Memory-aware curriculum federated learning for breast cancer classification

For early breast cancer detection, regular screening with mammography imaging is recommended. Routinary examinations result in datasets with a predominant amount of negative samples. A potential solution to such class-imbalance is joining forces across multiple institutions. Developing a collaborative computer-aided diagnosis system is challenging in different ways. Patient privacy and regulations need to be carefully respected. Data across institutions may be acquired from different devices or imaging protocols, leading to heterogeneous non-IID data. Also, for learning-based methods, new optimization strategies working on distributed data are required. Recently, federated learning has emerged as an effective tool for collaborative learning. In this setting, local models perform computation on their private data to update the global model. The order and the frequency of local updates influence the final global model. Hence, the order in which samples are locally presented to the optimizers plays an important role. In this work, we define a memory-aware curriculum learning method for the federated setting. Our curriculum controls the order of the training samples paying special attention to those that are forgotten after the deployment of the global model. Our approach is combined with unsupervised domain adaptation to deal with domain shift while preserving data privacy. We evaluate our method with three clinical datasets from different vendors. Our results verify the effectiveness of federated adversarial learning for the multi-site breast cancer classification. Moreover, we show that our proposed memory-aware curriculum method is beneficial to further improve classification performance. Our code is publicly available at: https://github.com/ameliajimenez/curriculum-federated-learning.

Machine Intelligence-Driven Classification of Cancer Patients-Derived Extracellular Vesicles using Fluorescence Correlation Spectroscopy: Results from a Pilot Study

Patient-derived extracellular vesicles (EVs) that contains a complex biological cargo is a valuable source of liquid biopsy diagnostics to aid in early detection, cancer screening, and precision nanotherapeutics. In this study, we predicted that coupling cancer patient blood-derived EVs to time-resolved spectroscopy and artificial intelligence (AI) could provide a robust cancer screening and follow-up tools. Methods: Fluorescence correlation spectroscopy (FCS) measurements were performed on 24 blood samples-derived EVs. Blood samples were obtained from 15 cancer patients (presenting 5 different types of cancers), and 9 healthy controls (including patients with benign lesions). The obtained FCS autocorrelation spectra were processed into power spectra using the Fast-Fourier Transform algorithm and subjected to various machine learning algorithms to distinguish cancer spectra from healthy control spectra. Results and Applications: The performance of AdaBoost Random Forest (RF) classifier, support vector machine, and multilayer perceptron, were tested on selected frequencies in the N=118 power spectra. The RF classifier exhibited a 90% classification accuracy and high sensitivity and specificity in distinguishing the FCS power spectra of cancer patients from those of healthy controls. Further, an image convolutional neural network (CNN), ResNet network, and a quantum CNN were assessed on the power spectral images as additional validation tools. All image-based CNNs exhibited a nearly equal classification performance with an accuracy of roughly 82% and reasonably high sensitivity and specificity scores. Our pilot study demonstrates that AI-algorithms coupled to time-resolved FCS power spectra can accurately and differentially classify the complex patient-derived EVs from different cancer samples of distinct tissue subtypes.

Visual Probing and Correction of Object Recognition Models with Interactive user feedback

With the advent of state-of-the-art machine learning and deep learning technologies, several industries are moving towards the field. Applications of such technologies are highly diverse ranging from natural language processing to computer vision. Object recognition is one such area in the computer vision domain. Although proven to perform with high accuracy, there are still areas where such models can be improved. This is in-fact highly important in real-world use cases like autonomous driving or cancer detection, that are highly sensitive and expect such technologies to have almost no uncertainties. In this paper, we attempt to visualise the uncertainties in object recognition models and propose a correction process via user feedback. We further demonstrate our approach on the data provided by the VAST 2020 Mini-Challenge 2.

MHSnet: Multi-head and Spatial Attention Network with False-Positive Reduction for Pulmonary Nodules Detection

The mortality of lung cancer has ranked high among cancers for many years. Early detection of lung cancer is critical for disease prevention, cure, and mortality rate reduction. However, existing detection methods on pulmonary nodules introduce an excessive number of false positive proposals in order to achieve high sensitivity, which is not practical in clinical situations. In this paper, we propose the multi-head detection and spatial squeeze-and-attention network, MHSnet, to detect pulmonary nodules, in order to aid doctors in the early diagnosis of lung cancers. Specifically, we first introduce multi-head detectors and skip connections to customize for the variety of nodules in sizes, shapes and types and capture multi-scale features. Then, we implement a spatial attention module to enable the network to focus on different regions differently inspired by how experienced clinicians screen CT images, which results in fewer false positive proposals. Lastly, we present a lightweight but effective false positive reduction module with the Linear Regression model to cut down the number of false positive proposals, without any constraints on the front network. Extensive experimental results compared with the state-of-the-art models have shown the superiority of the MHSnet in terms of the average FROC, sensitivity and especially false discovery rate (2.98% and 2.18% improvement in terms of average FROC and sensitivity, 5.62% and 28.33% decrease in terms of false discovery rate and average candidates per scan). The false positive reduction module significantly decreases the average number of candidates generated per scan by 68.11% and the false discovery rate by 13.48%, which is promising to reduce distracted proposals for the downstream tasks based on the detection results.

Proposing method to Increase the detection accuracy of stomach cancer based on colour and lint features of tongue using CNN and SVM

Today, gastric cancer is one of the diseases which affected many people's life. Early detection and accuracy are the main and crucial challenges in finding this kind of cancer. In this paper, a method to increase the accuracy of the diagnosis of detecting cancer using lint and colour features of tongue based on deep convolutional neural networks and support vector machine is proposed. In the proposed method, the region of tongue is first separated from the face image by {deep RCNN} \color{black} Recursive Convolutional Neural Network (R-CNN) \color{black}. After the necessary preprocessing, the images to the convolutional neural network are provided and the training and test operations are triggered. The results show that the proposed method is correctly able to identify the area of the tongue as well as the patient's person from the non-patient. Based on experiments, the DenseNet network has the highest accuracy compared to other deep architectures. The experimental results show that the accuracy of this network for gastric cancer detection reaches 91% which shows the superiority of method in comparison to the state-of-the-art methods.

Colon Nuclei Instance Segmentation using a Probabilistic Two-Stage Detector

Cancer is one of the leading causes of death in the developed world. Cancer diagnosis is performed through the microscopic analysis of a sample of suspicious tissue. This process is time consuming and error prone, but Deep Learning models could be helpful for pathologists during cancer diagnosis. We propose to change the CenterNet2 object detection model to also perform instance segmentation, which we call SegCenterNet2. We train SegCenterNet2 in the CoNIC challenge dataset and show that it performs better than Mask R-CNN in the competition metrics.

Overhead MNIST: A Benchmark Satellite Dataset

The research presents an overhead view of 10 important objects and follows the general formatting requirements of the most popular machine learning task: digit recognition with MNIST. This dataset offers a public benchmark extracted from over a million human-labelled and curated examples. The work outlines the key multi-class object identification task while matching with prior work in handwriting, cancer detection, and retail datasets. A prototype deep learning approach with transfer learning and convolutional neural networks (MobileNetV2) correctly identifies the ten overhead classes with an average accuracy of 96.7%. This model exceeds the peak human performance of 93.9%. For upgrading satellite imagery and object recognition, this new dataset benefits diverse endeavors such as disaster relief, land use management, and other traditional remote sensing tasks. The work extends satellite benchmarks with new capabilities to identify efficient and compact algorithms that might work on-board small satellites, a practical task for future multi-sensor constellations. The dataset is available on Kaggle and Github.

Comparison of different CNNs for breast tumor classification from ultrasound images

Breast cancer is one of the deadliest cancer worldwide. Timely detection could reduce mortality rates. In the clinical routine, classifying benign and malignant tumors from ultrasound (US) imaging is a crucial but challenging task. An automated method, which can deal with the variability of data is therefore needed. In this paper, we compared different Convolutional Neural Networks (CNNs) and transfer learning methods for the task of automated breast tumor classification. The architectures investigated in this study were VGG-16 and Inception V3. Two different training strategies were investigated: the first one was using pretrained models as feature extractors and the second one was to fine-tune the pre-trained models. A total of 947 images were used, 587 corresponded to US images of benign tumors and 360 with malignant tumors. 678 images were used for the training and validation process, while 269 images were used for testing the models. Accuracy and Area Under the receiver operating characteristic Curve (AUC) were used as performance metrics. The best performance was obtained by fine tuning VGG-16, with an accuracy of 0.919 and an AUC of 0.934. The obtained results open the opportunity to further investigation with a view of improving cancer detection.

Detecting and analysing spontaneous oral cancer speech in the wild

Oral cancer speech is a disease which impacts more than half a million people worldwide every year. Analysis of oral cancer speech has so far focused on read speech. In this paper, we 1) present and 2) analyse a three-hour long spontaneous oral cancer speech dataset collected from YouTube. 3) We set baselines for an oral cancer speech detection task on this dataset. The analysis of these explainable machine learning baselines shows that sibilants and stop consonants are the most important indicators for spontaneous oral cancer speech detection.