Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy

Anomaly detection methods generally target the learning of a normal image distribution (i.e., inliers showing healthy cases) and during testing, samples relatively far from the learned distribution are classified as anomalies (i.e., outliers showing disease cases). These approaches tend to be sensitive to outliers that lie relatively close to inliers (e.g., a colonoscopy image with a small polyp). In this paper, we address the inappropriate sensitivity to outliers by also learning from inliers. We propose a new few-shot anomaly detection method based on an encoder trained to maximise the mutual information between feature embeddings and normal images, followed by a few-shot score inference network, trained with a large set of inliers and a substantially smaller set of outliers. We evaluate our proposed method on the clinical problem of detecting frames containing polyps from colonoscopy video sequences, where the training set has 13350 normal images (i.e., without polyps) and less than 100 abnormal images (i.e., with polyps). The results of our proposed model on this data set reveal a state-of-the-art detection result, while the performance based on different number of anomaly samples is relatively stable after approximately 40 abnormal training images.Comment: Accept at MICCAI 202

Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy

There are two challenges associated with the interpretability of deep learning models in medical image analysis applications that need to be addressed: confidence calibration and classification uncertainty. Confidence calibration associates the classification probability with the likelihood that it is actually correct – hence, a sample that is classified with confidence X% has a chance of X% of being correctly classified. Classification uncertainty estimates the noise present in the classification process, where such noise estimate can be used to assess the reliability of a particular classification result. Both confidence calibration and classification uncertainty are considered to be helpful in the interpretation of a classification result produced by a deep learning model, but it is unclear how much they affect classification accuracy and calibration, and how they interact. In this paper, we study the roles of confidence calibration (via post-process temperature scaling) and classification uncertainty (computed either from classification entropy or the predicted variance produced by Bayesian methods) in deep learning models. Results suggest that calibration and uncertainty improve classification interpretation and accuracy. This motivates us to propose a new Bayesian deep learning method that relies both on calibration and uncertainty to improve classification accuracy and model interpretability. Experiments are conducted on a recently proposed five-class polyp classification problem, using a data set containing 940 high-quality images of colorectal polyps, and results indicate that our proposed method holds the state-of-the-art results in terms of confidence calibration and classification accuracy.Gustavo Carneiro, Leonardo Zorron Cheng Tao Pu, Rajvinder Singh, Alastair Bur

Sessile serrated adenoma/polyps: Where are we at in 2016?

It is currently known that colorectal cancers (CRC) arise from 3 different pathways: the adenoma to carcinoma chromosomal instability pathway (50%-70%); the mutator “Lynch syndrome” route (3%-5%); and the serrated pathway (30%-35%). The World Health Organization has classified serrated polyps into three types of lesions: hyperplastic polyps (HP), sessile serrated adenomas/polyps (SSA/P) and traditional serrated adenomas (TSA), the latter two strongly associated with development of CRCs. HPs do not cause cancer and TSAs are rare. SSA/P appear to be the responsible precursor lesion for the development of cancers through the serrated pathway. Both HPs and SSA/Ps appear morphologically similar. SSA/P are difficult to detect. The margins are normally inconspicuous. En bloc resection of these polyps can hence be troublesome. A careful examination of borders, submucosal injection of a dye solution (for larger lesions) and resection of a rim of normal tissue around the lesion may ensure total eradication of these lesions

Conditioning training and retrieval increase phospholipase A(2) activity in the cerebral cortex of rats

In rats, phospholipase A(2) (PLA(2)) activity was found to be increased in the hippocampus immediately after training and retrieval of a contextual fear conditioning paradigm (step-down inhibitory avoidance [IA] task). In the present study we investigated whether PLA(2) is also activated in the cerebral cortex of rats in association with contextual fear learning and retrieval. We observed that IA training induces a rapid (immediately after training) and long-lasting (3 h after training) activation of PLA(2) in both frontal and parietal cortices. However, immediately after retrieval (measured 24 h after training), PLA(2) activity was increased just in the parietal cortex. These findings suggest that PLA(2) activity is differentially required in the frontal and parietal cortices for the mechanisms of contextual learning and retrieval. Because reduced brain PLA(2) activity has been reported in Alzheimer disease, our results suggest that stimulation of PLA(2) activity may offer new treatment strategies for this disease

Outcomes of endoscopic ultrasound as a one-off pancreatic cancer screening tool for 122 high- and moderate-risk patients

Background and Aim Pancreatic cancer (PC) carries a poor prognosis and is often detected at later stages. Screening programs for moderate‐ and high‐risk people are still under debate. We present the results from a prospective study on endoscopic ultrasound (EUS) as a one‐off screening tool for pancreatic cancer screening. Methods Asymptomatic patients with moderate‐ or high‐risk of PC were invited to participate. Moderate risk consisted of one first‐degree and at least one second‐degree relative with PC and no PC‐associated genetic mutations. High risk consisted of >1 first‐degree relatives with PC or PC‐associated mutations (i.e. BRCA2, Lynch Syndrome, Familial Atypical Multiple Mole Melanoma Syndrome, STK11, or PALB2). All included patients had genetic counseling and a screening EUS done. Primary outcome was the detection of PC on EUS. Secondary outcomes assessed the evolution of psychological symptoms based on the Impact of Events Scale (IES) and Personal Consequences Questionnaire (PCQ) before and after the screening took place. Results A total of 122 patients had a screening EUS performed between 2013 and 2019; 60 were male, 55.8 years was the mean age, 78 were at high risk for PC, and 25 had PC‐associated mutations. No pancreatic cancers were identified at the one‐off EUS screening. Overall, patients' IES/PCQ scores did not change after screening and feedback of no malignancy, with the exception of females (less concerned about PC after screening EUS). Conclusions EUS did not detect any PCs in either a moderate‐ or high‐risk population as a one‐off screening method. The EUS procedure and genetic counseling improved psychological symptoms for the female subset of this population