Towards Optimal Patch Size in Vision Transformers for Tumor Segmentation

Detection of tumors in metastatic colorectal cancer (mCRC) plays an essential role in the early diagnosis and treatment of liver cancer. Deep learning models backboned by fully convolutional neural networks (FCNNs) have become the dominant model for segmenting 3D computerized tomography (CT) scans. However, since their convolution layers suffer from limited kernel size, they are not able to capture long-range dependencies and global context. To tackle this restriction, vision transformers have been introduced to solve FCNN's locality of receptive fields. Although transformers can capture long-range features, their segmentation performance decreases with various tumor sizes due to the model sensitivity to the input patch size. While finding an optimal patch size improves the performance of vision transformer-based models on segmentation tasks, it is a time-consuming and challenging procedure. This paper proposes a technique to select the vision transformer's optimal input multi-resolution image patch size based on the average volume size of metastasis lesions. We further validated our suggested framework using a transfer-learning technique, demonstrating that the highest Dice similarity coefficient (DSC) performance was obtained by pre-training on training data with a larger tumour volume using the suggested ideal patch size and then training with a smaller one. We experimentally evaluate this idea through pre-training our model on a multi-resolution public dataset. Our model showed consistent and improved results when applied to our private multi-resolution mCRC dataset with a smaller average tumor volume. This study lays the groundwork for optimizing semantic segmentation of small objects using vision transformers. The implementation source code is available at:https://github.com/Ramtin-Mojtahedi/OVTPS

Parameter-Efficient Methods for Metastases Detection from Clinical Notes

Understanding the progression of cancer is crucial for defining treatments for patients. The objective of this study is to automate the detection of metastatic liver disease from free-style computed tomography (CT) radiology reports. Our research demonstrates that transferring knowledge using three approaches can improve model performance. First, we utilize generic language models (LMs), pretrained in a self-supervised manner. Second, we use a semi-supervised approach to train our model by automatically annotating a large unlabeled dataset; this approach substantially enhances the model's performance. Finally, we transfer knowledge from related tasks by designing a multi-task transfer learning methodology. We leverage the recent advancement of parameter-efficient LM adaptation strategies to improve performance and training efficiency. Our dataset consists of CT reports collected at Memorial Sloan Kettering Cancer Center (MSKCC) over the course of 12 years. 2,641 reports were manually annotated by domain experts; among them, 841 reports have been annotated for the presence of liver metastases. Our best model achieved an F1-score of 73.8%, a precision of 84%, and a recall of 65.8%.Comment: 6 pages, 1 figure, The 36th Canadian Conference on Artificial Intelligenc

Liver imaging reporting and data system: An expert consensus statement

The increasing incidence and high morbidity and mortality of hepatocellular carcinoma (HCC) have inspired the creation of the Liver Imaging Reporting and Data System (LI-RADS). LI-RADS aims to reduce variability in exam interpretation, improve communication, facilitate clinical therapeutic decisions, reduce omission of pertinent information, and facilitate the monitoring of outcomes. LI-RADS is a dynamic process, which is updated frequently. In this article, we describe the LI-RADS 2014 version (v2014), which marks the second update since the initial version in 2011

Liver imaging : it is time to adopt standardized terminology

Liver imaging plays a vital role in the management of patients at risk for hepatocellular carcinoma (HCC); however, progress in the field is challenged by nonuniform and inconsistent terminology in the published literature. The Steering Committee of the American College of Radiology (ACR)’s Liver Imaging Reporting And Data System (LI-RADS), in conjunction with the LI-RADS Lexicon Writing Group and the LI-RADS International Working Group, present this consensus document to establish a single universal liver imaging lexicon. The lexicon is intended for use in research, education, and clinical care of patients at risk for HCC (i.e., the LI-RADS population) and in the general population (i.e., even when LI-RADS algorithms are not applicable). We anticipate that the universal adoption of this lexicon will provide research, educational, and clinical benefits

Induction of humoral immune response to multiple recombinant Rhipicephalus appendiculatus antigens and their effect on tick feeding success and pathogen transmission

BACKGROUND: Rhipicephalus appendiculatus is the primary vector of Theileria parva, the etiological agent of East Coast fever (ECF), a devastating disease of cattle in sub-Saharan Africa. We hypothesized that a vaccine targeting tick proteins that are involved in attachment and feeding might affect feeding success and possibly reduce tick-borne transmission of T. parva. Here we report the evaluation of a multivalent vaccine cocktail of tick antigens for their ability to reduce R. appendiculatus feeding success and possibly reduce tick-transmission of T. parva in a natural host-tick-parasite challenge model. METHODS: Cattle were inoculated with a multivalent antigen cocktail containing recombinant tick protective antigen subolesin as well as two additional R. appendiculatus saliva antigens: the cement protein TRP64, and three different histamine binding proteins. The cocktail also contained the T. parva sporozoite antigen p67C. The effect of vaccination on the feeding success of nymphal and adult R. appendiculatus ticks was evaluated together with the effect on transmission of T. parva using a tick challenge model. RESULTS: To our knowledge, this is the first evaluation of the anti-tick effects of these antigens in the natural host-tick-parasite combination. In spite of evidence of strong immune responses to all of the antigens in the cocktail, vaccination with this combination of tick and parasite antigens did not appear to effect tick feeding success or reduce transmission of T. parva. CONCLUSION: The results of this study highlight the importance of early evaluation of anti-tick vaccine candidates in biologically relevant challenge systems using the natural tick-host-parasite combination

The quest for the solar g modes

Solar gravity modes (or g modes) -- oscillations of the solar interior for which buoyancy acts as the restoring force -- have the potential to provide unprecedented inference on the structure and dynamics of the solar core, inference that is not possible with the well observed acoustic modes (or p modes). The high amplitude of the g-mode eigenfunctions in the core and the evanesence of the modes in the convection zone make the modes particularly sensitive to the physical and dynamical conditions in the core. Owing to the existence of the convection zone, the g modes have very low amplitudes at photospheric levels, which makes the modes extremely hard to detect. In this paper, we review the current state of play regarding attempts to detect g modes. We review the theory of g modes, including theoretical estimation of the g-mode frequencies, amplitudes and damping rates. Then we go on to discuss the techniques that have been used to try to detect g modes. We review results in the literature, and finish by looking to the future, and the potential advances that can be made -- from both data and data-analysis perspectives -- to give unambiguous detections of individual g modes. The review ends by concluding that, at the time of writing, there is indeed a consensus amongst the authors that there is currently no undisputed detection of solar g modes.Comment: 71 pages, 18 figures, accepted by Astronomy and Astrophysics Revie

Identification and functional characterisation of CRK12:CYC9, a novel cyclin-dependent kinase (CDK)-cyclin complex in Trypanosoma brucei

The protozoan parasite, Trypanosoma brucei, is spread by the tsetse fly and causes trypanosomiasis in humans and animals. Both the life cycle and cell cycle of the parasite are complex. Trypanosomes have eleven cdc2-related kinases (CRKs) and ten cyclins, an unusually large number for a single celled organism. To date, relatively little is known about the function of many of the CRKs and cyclins, and only CRK3 has previously been shown to be cyclin-dependent in vivo. Here we report the identification of a previously uncharacterised CRK:cyclin complex between CRK12 and the putative transcriptional cyclin, CYC9. CRK12:CYC9 interact to form an active protein kinase complex in procyclic and bloodstream T. brucei. Both CRK12 and CYC9 are essential for the proliferation of bloodstream trypanosomes in vitro, and we show that CRK12 is also essential for survival of T. brucei in a mouse model, providing genetic validation of CRK12:CYC9 as a novel drug target for trypanosomiasis. Further, functional characterisation of CRK12 and CYC9 using RNA interference reveals roles for these proteins in endocytosis and cytokinesis, respectively