Developments in molecular and advanced endoscopic imaging in esophageal cancer

Esophageal cancer, including esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC), shows high incidence and poor prognosis. The early detection and endoscopic treatment of (pre)malignant lesions of esophageal cancer significantly improve disease outcomes of patients. However, the high-definition white-light endoscopy followed by random biopsy is reported with a non-ignorable miss rate. The development of advanced endoscopic techniques, such as fluorescence molecular endoscopy (FME) and endocytoscopy, can aid endoscopists in diagnosing early (pre)malignant lesions in vivo. FME realizes wide-field molecular imaging under endoscopy, which serves as a red flag technique for endoscopists by fluorescently highlighting the disease-specific molecule. In Chapter 3 and 4, we identified suitable target proteins and developed near-infrared fluorescent tracers for FME to detect ESCC and EAC at an early stage. In Chapter 6, we investigated the feasibility of assessing pathological response of EAC patients after neoadjuvant chemoradiotherapy by Bevacizumab-800CW guided FME.Endocytoscopy is a pin-point imaging technique that provides endoscopists with magnified optical cellular morphology and subcellular characteristics, referred to as an optical biopsy. In Chapter 5, we developed a classification criteria, an online training module for clinicians and a computer-aided diagnosis (CAD) algorithm based on in vivo images of fourth-generation endocytoscopy to distinguish dysplastic from non-dysplastic Barrett's esophagus tissue. We further investigated the interaction of this CAD algorithm with the clinicians

A Simple Parametric Classification Baseline for Generalized Category Discovery

Generalized category discovery (GCD) is a problem setting where the goal is to discover novel categories within an unlabelled dataset using the knowledge learned from a set of labelled samples. Recent works in GCD argue that a non-parametric classifier formed using semi-supervised $k$-means can outperform strong baselines which use parametric classifiers as it can alleviate the over-fitting to seen categories in the labelled set. In this paper, we revisit the reason that makes previous parametric classifiers fail to recognise new classes for GCD. By investigating the design choices of parametric classifiers from the perspective of model architecture, representation learning, and classifier learning, we conclude that the less discriminative representations and unreliable pseudo-labelling strategy are key factors that make parametric classifiers lag behind non-parametric ones. Motivated by our investigation, we present a simple yet effective parametric classification baseline that outperforms the previous best methods by a large margin on multiple popular GCD benchmarks. We hope the investigations and the simple baseline can serve as a cornerstone to facilitate future studies. Our code is available at: https://github.com/CVMI-Lab/SimGCD.Comment: Code: https://github.com/CVMI-Lab/SimGC

Today's Mistakes and Tomorrow's Wisdom in Endoscopic Imaging of Barrett's Esophagus

Background: Esophageal adenocarcinoma (EAC) is one of the main causes of cancer-related deaths worldwide and its incidence is rising. Barrett's esophagus (BE) can develop low- and high-grade dysplasia which can progress to EAC overtime. The golden standard to detect dysplastic BE (DBE) or EAC is surveillance with high-definition white-light endoscopy (HD-WLE) and random biopsies according to the Seattle protocol. However, this method is time-consuming and associated with a remarkable miss rate. Therefore, there is great need for the development of novel reliable techniques to optimize surveillance strategies and improve detection rates.Summary: Optical chromoendoscopy (OC) techniques like narrow-band imaging have shown improved detection of DBE and EAC compared to HD-WLE and random biopsies. Most recent OC techniques, including the iSCAN optical enhancement system and linked color imaging, showed improved characterization of DBE and EAC retrospectively. Fluorescence molecular endoscopy (FME) presented promising results to highlight DBE and EAC. Moreover, with the establishment of well-performing delineation computer-aided detection (CAD) algorithms and the first real-time CAD system for EAC, we expect clinical application of CAD in the near future.Key Messages: Despite impressive progress made in the development of advanced endoscopic techniques, combined HD-WLE/OC followed by random biopsies remains the golden standard for BE surveillance. Surveillance depends on appropriate mucosal cleansing, sufficient inspection time, and competence of the performing gastroenterologist to improve detection of EAC. In addition, to facilitate the clinical implementation of advanced endoscopic techniques, multicenter prospective clinical studies are demanded for OC and FME. Meanwhile, further optimization of CAD algorithms, the education of gastroenterologists, and analysis of the interaction between the clinician and the computer should be performed.</p

Bis(2-hydroxy-N′-isopropylidenebenzo­hydrazidato-κ2 N′,O)bis­(pyridine-κN)cobalt(II)

In the title complex, [Co(C10H11N2O2)2(C5H5N)2], the CoII atom lies on a centre of symmetry and adopts a distorted cis-CoO2N4 octa­hedral geometry. The two acetone salicyloylhydrazone ligands are deprotonated and act as N,O-bidentate monoanionic ligands, forming the equatorial plane, while the axial positions are occupied by two N atoms of two pyridine mol­ecules. The complex presents O—H⋯N and C—H⋯N intra­molecular hydrogen bonds. Inter­molecular C—H⋯N and C—H⋯O inter­actions are also present in the crystal

Self-Supervised Visual Representation Learning with Semantic Grouping

In this paper, we tackle the problem of learning visual representations from unlabeled scene-centric data. Existing works have demonstrated the potential of utilizing the underlying complex structure within scene-centric data; still, they commonly rely on hand-crafted objectness priors or specialized pretext tasks to build a learning framework, which may harm generalizability. Instead, we propose contrastive learning from data-driven semantic slots, namely SlotCon, for joint semantic grouping and representation learning. The semantic grouping is performed by assigning pixels to a set of learnable prototypes, which can adapt to each sample by attentive pooling over the feature and form new slots. Based on the learned data-dependent slots, a contrastive objective is employed for representation learning, which enhances the discriminability of features, and conversely facilitates grouping semantically coherent pixels together. Compared with previous efforts, by simultaneously optimizing the two coupled objectives of semantic grouping and contrastive learning, our approach bypasses the disadvantages of hand-crafted priors and is able to learn object/group-level representations from scene-centric images. Experiments show our approach effectively decomposes complex scenes into semantic groups for feature learning and significantly benefits downstream tasks, including object detection, instance segmentation, and semantic segmentation. Code is available at: https://github.com/CVMI-Lab/SlotCon.Comment: Accepted at NeurIPS 202

Surface Decomposition of Doped PrBaMn₂O_5+δInduced by in Situ Nanoparticle Exsolution:Quantitative Characterization and Catalytic Effect in Methane Dry Reforming Reaction

The exsolution of metallic nanoparticles (NPs) from perovskite oxides is a promising strategy for synthesizing supported catalysts. The associated segregation of A-site cations on the surface is challenging to investigate experimentally and is often detrimental to the catalytic performance. In this work, we found that during the in situ exsolution of Ni-Co bimetallic nanoparticles from Pr0.45Ba0.45Mn1–x(Co1/3 Ni2/3)xO3±δ, A-site cation enrichment occurred on the surface when x is 0.1; yet, the perovskite surface decomposed when x reached 0.2, forming a thin layer comprising various nanocrystalline oxides, which partially blocked the active sites of the exsolved Ni-Co particles. A hydration and carbonation reaction facilitated the conversion of nanocrystalline BaO species into large and highly crystallized BaCO3 particles. This enabled the exposure of more Ni-Co active sites and offered a chance to quantify that the decomposed surface layer accounts for ∼7.2 wt % of the total perovskite. Because of this unique feature, the surface-decomposed catalyst showed higher activity in the dry methane reforming reaction with better stability. Importantly, the regeneration feature was not hampered as the complete exsolution-dissolution recyclability of the catalyst remained