Can we do that simpler? Simple, Efficient, High-Quality Evaluation Metrics for NLG

We explore efficient evaluation metrics for Natural Language Generation (NLG). To implement efficient metrics, we replace (i) computation-heavy transformers in metrics such as BERTScore, MoverScore, BARTScore, XMoverScore, etc. with lighter versions (such as distilled ones) and (ii) cubic inference time alignment algorithms such as Word Mover Distance with linear and quadratic approximations. We consider six evaluation metrics (both monolingual and multilingual), assessed on three different machine translation datasets, and 16 light-weight transformers as replacement. We find, among others, that (a) TinyBERT shows best quality-efficiency tradeoff for semantic similarity metrics of the BERTScore family, retaining 97\% quality and being 5x faster at inference time on average, (b) there is a large difference in speed-ups on CPU vs. GPU (much higher speed-ups on CPU), and (c) WMD approximations yield no efficiency gains but lead to a substantial drop in quality on 2 out of 3 datasets we examine.Comment: Work in progres

Valuing vicinity: Memory attention framework for context-based semantic segmentation in histopathology

The segmentation of histopathological whole slide images into tumourous and non-tumourous types of tissue is a challenging task that requires the consideration of both local and global spatial contexts to classify tumourous regions precisely. The identification of subtypes of tumour tissue complicates the issue as the sharpness of separation decreases and the pathologist’s reasoning is even more guided by spatial context. However, the identification of detailed tissue types is crucial for providing personalized cancer therapies. Due to the high resolution of whole slide images, existing semantic segmentation methods, restricted to isolated image sections, are incapable of processing context information beyond. To take a step towards better context comprehension, we propose a patch neighbour attention mechanism to query the neighbouring tissue context from a patch embedding memory bank and infuse context embeddings into bottleneck hidden feature maps. Our memory attention framework (MAF) mimics a pathologist’s annotation procedure — zooming out and considering surrounding tissue context. The framework can be integrated into any encoder–decoder segmentation method. We evaluate the MAF on two public breast cancer and liver cancer data sets and an internal kidney cancer data set using famous segmentation models (U-Net, DeeplabV3) and demonstrate the superiority over other context-integrating algorithms — achieving a substantial improvement of up to 17% on Dice score

Imaging and targeted therapy of pancreatic ductal adenocarcinoma using the theranostic sodium iodide symporter (NIS) gene

The theranostic sodium iodide symporter (NIS) gene allows detailed molecular imaging of transgene expression and application of therapeutic radionuclides. As a crucial step towards clinical application, we investigated tumor specificity and transfection efficiency of epidermal growth factor receptor (EGFR)-targeted polyplexes as systemic NIS gene delivery vehicles in an advanced genetically engineered mouse model of pancreatic ductal adenocarcinoma (PDAC) that closely reflects human disease. PDAC was induced in mice by pancreas-specific activation of constitutively active Kras(G12D) and deletion of Trp53. We used tumor-targeted polyplexes (LPEIPEG-GE11/NIS) based on linear polyethylenimine, shielded by polyethylene glycol and coupled with the EGFR-specific peptide ligand GE11, to target a NIS-expressing plasmid to high EGFR-expressing PDAC. In vitro iodide uptake studies in cell explants from murine EGFR-positive and EGFR-ablated PDAC lesions demonstrated high transfection efficiency and EGFR-specificity of LPEI-PEG-GE11/NIS. In vivo I-123 gamma camera imaging and three-dimensional high-resolution I-124 PET showed significant tumor-specific accumulation of radioiodide after systemic LPEI-PEG-GE11/NIS injection. Administration of I-131 in LPEI-PEG-GE11/NIS-treated mice resulted in significantly reduced tumor growth compared to controls as determined by magnetic resonance imaging, though survival was not significantly prolonged. This study opens the exciting prospect of NIS-mediated radionuclide imaging and therapy of PDAC after systemic non-viral NIS gene delivery

Unifying generative and discriminative learning principles

<p>Abstract</p> <p>Background</p> <p>The recognition of functional binding sites in genomic DNA remains one of the fundamental challenges of genome research. During the last decades, a plethora of different and well-adapted models has been developed, but only little attention has been payed to the development of different and similarly well-adapted learning principles. Only recently it was noticed that discriminative learning principles can be superior over generative ones in diverse bioinformatics applications, too.</p> <p>Results</p> <p>Here, we propose a generalization of generative and discriminative learning principles containing the maximum likelihood, maximum a posteriori, maximum conditional likelihood, maximum supervised posterior, generative-discriminative trade-off, and penalized generative-discriminative trade-off learning principles as special cases, and we illustrate its efficacy for the recognition of vertebrate transcription factor binding sites.</p> <p>Conclusions</p> <p>We find that the proposed learning principle helps to improve the recognition of transcription factor binding sites, enabling better computational approaches for extracting as much information as possible from valuable wet-lab data. We make all implementations available in the open-source library Jstacs so that this learning principle can be easily applied to other classification problems in the field of genome and epigenome analysis.</p

Apples and oranges: avoiding different priors in Bayesian DNA sequence analysis

<p>Abstract</p> <p>Background</p> <p>One of the challenges of bioinformatics remains the recognition of short signal sequences in genomic DNA such as donor or acceptor splice sites, splicing enhancers or silencers, translation initiation sites, transcription start sites, transcription factor binding sites, nucleosome binding sites, miRNA binding sites, or insulator binding sites. During the last decade, a wealth of algorithms for the recognition of such DNA sequences has been developed and compared with the goal of improving their performance and to deepen our understanding of the underlying cellular processes. Most of these algorithms are based on statistical models belonging to the family of Markov random fields such as position weight matrix models, weight array matrix models, Markov models of higher order, or moral Bayesian networks. While in many comparative studies different learning principles or different statistical models have been compared, the influence of choosing different prior distributions for the model parameters when using different learning principles has been overlooked, and possibly lead to questionable conclusions.</p> <p>Results</p> <p>With the goal of allowing direct comparisons of different learning principles for models from the family of Markov random fields based on the <it>same a-priori information</it>, we derive a generalization of the commonly-used product-Dirichlet prior. We find that the derived prior behaves like a Gaussian prior close to the maximum and like a Laplace prior in the far tails. In two case studies, we illustrate the utility of the derived prior for a direct comparison of different learning principles with different models for the recognition of binding sites of the transcription factor Sp1 and human donor splice sites.</p> <p>Conclusions</p> <p>We find that comparisons of different learning principles using the same a-priori information can lead to conclusions different from those of previous studies in which the effect resulting from different priors has been neglected. We implement the derived prior is implemented in the open-source library Jstacs to enable an easy application to comparative studies of different learning principles in the field of sequence analysis.</p

Systemic treatment of advanced/metastatic renal cell carcinoma in the context of SARS-CoV-2 pandemic: recommendations from the interdisciplinary working group for renal tumors (IAG-N)

Abstract This letter summarizes recommendations from the interdisciplinary working group of renal tumors (IAGN) of the German Cancer Society for the systemic treatment of advanced/metastatic renal cell carcinoma in the context of the current SARS-CoV-2 pandemi

CellViT: Vision Transformers for Precise Cell Segmentation and Classification

Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images are important clinical tasks and crucial for a wide range of applications. However, it is a challenging task due to nuclei variances in staining and size, overlapping boundaries, and nuclei clustering. While convolutional neural networks have been extensively used for this task, we explore the potential of Transformer-based networks in this domain. Therefore, we introduce a new method for automated instance segmentation of cell nuclei in digitized tissue samples using a deep learning architecture based on Vision Transformer called CellViT. CellViT is trained and evaluated on the PanNuke dataset, which is one of the most challenging nuclei instance segmentation datasets, consisting of nearly 200,000 annotated Nuclei into 5 clinically important classes in 19 tissue types. We demonstrate the superiority of large-scale in-domain and out-of-domain pre-trained Vision Transformers by leveraging the recently published Segment Anything Model and a ViT-encoder pre-trained on 104 million histological image patches - achieving state-of-the-art nuclei detection and instance segmentation performance on the PanNuke dataset with a mean panoptic quality of 0.51 and an F1-detection score of 0.83. The code is publicly available at https://github.com/TIO-IKIM/CellViTComment: 13 pages, 5 figures, appendix include

Combined immune checkpoint blockade (anti-PD-1/anti-CTLA-4): Evaluation and management of adverse drug reactions

Background: Combined immune checkpoint blockade (ICB) provides unprecedented efficacy gains in numerous cancer indications, with PD-1 inhibitor nivolumab plus CTLA-4 inhibitor ipilimumab in advanced melanoma as first-ever approved therapies for combined ICB. However, gains in efficacy must be balanced against a higher frequency and severity of adverse drug reactions (ADR). Because delays in diagnosis and management might result in symptom worsening and further complications, clinicians shall be well trained to identify ADR promptly and monitor patients adequately. This paper reviews safety data assessed by the European Medicines Agency for the anti-PD-1/CTLA-4 combination and provides a literature overview on published case reports for rare ADR with suspected potential underreporting. Incidences and kinetics of immune-related ADR are described. Recommendations for the evaluation and management of ADR are convened by an interdisciplinary expert panel focusing on rare but clinically important side effects arising from combined ICB. Background: Pooled safety data from 1551 patients with advanced melanoma, treated either with 3 mg/kg ipilimumab plus 1 mg/kg nivolumab (N = 407), or nivolumab alone (N = 787), or ipilimumab alone (N = 357) demonstrate that immune-related ADR occur more frequently for the combination, with a shorter time-to-onset, and tend to be more severe. The majority of events is reversible after systemic use of glucocorticoids, notably methylprednisolone or equivalents;in certain cases of long-lasting and refractory immune toxicities, non-steroidal immunosuppressants may be used, once ICB is interrupted or terminated. Combined ICB has considerable toxicities, therefore close monitoring and high experience in diagnosis and treatment of ADR is necessary