Self-rule to multi-adapt: Generalized multi-source feature learning using unsupervised domain adaptation for colorectal cancer tissue detection.

Supervised learning is constrained by the availability of labeled data, which are especially expensive to acquire in the field of digital pathology. Making use of open-source data for pre-training or using domain adaptation can be a way to overcome this issue. However, pre-trained networks often fail to generalize to new test domains that are not distributed identically due to tissue stainings, types, and textures variations. Additionally, current domain adaptation methods mainly rely on fully-labeled source datasets. In this work, we propose Self-Rule to Multi-Adapt (SRMA), which takes advantage of self-supervised learning to perform domain adaptation, and removes the necessity of fully-labeled source datasets. SRMA can effectively transfer the discriminative knowledge obtained from a few labeled source domain's data to a new target domain without requiring additional tissue annotations. Our method harnesses both domains' structures by capturing visual similarity with intra-domain and cross-domain self-supervision. Moreover, we present a generalized formulation of our approach that allows the framework to learn from multiple source domains. We show that our proposed method outperforms baselines for domain adaptation of colorectal tissue type classification in single and multi-source settings, and further validate our approach on an in-house clinical cohort. The code and trained models are available open-source: https://github.com/christianabbet/SRA

Forgotten Edible alpine plants in the canton of Valais

Tradition possesses plenty of forgotten wild edible plants and may help researchers in the quest for new food varieties. Swiss alpine cantons, especially the canton of Valais, have still had a viable tradition. However, societal changes and extensive urbanization have caused this knowledge to be confined to lateral valleys. This contribution aimed to document wild edible plants which were collected in the canton of Valais. 38 informants originating from four different valleys of the canton (Val d’Entremont, Val d’Illiez, Val d’Hérens, and Val d’Anniviers) were interviewed with semi-directive interviews. 98 edible plant species, which belong to 38 families, were identified. Plants were classified in eight categories based on the way they were traditionally used including salads, cooked vegetables, spices, alcoholic drinks, teas, syrups, jams, and raw snacks. The categories with the highest number of citations were teas (18%), followed by cooked vegetables (16%), jams (16%), and raw snacks (16%). Taraxacum officinale, Sambucus nigra, Chenopodium bonus-henricus, and Urtica dioica were the most cited plants and most commonly used in the different valleys. Knowledge on edible plants is found from its origins in agriculture and activities as shepherds. Books written in the XIXth and early XXth centuries have documented these uses and have allowed identification of around 40 food plants, which had already fallen in oblivion (e.g. Bunium bulbocastanum). Two traditional edible plants (Phyteuma orbiculare and Cirsium spinosissimum) were submitted to a thorough phytochemical investigation. Each plant was successively extracted with dichloromethane and methanol. Extracts were subjected to HPLC-MS DAD analyses and pure constituents were isolated by preparative and semi-preparative methods (Diaion HP-20, liquid-liquid extraction, Sephadex LH-20, open column on silica gel, preparative and semi-preparative columns on C18). The molecular structures of the isolated compounds were elucidated by chemical and spectroscopic methods. In addition, substances relevant for nutrition (e.g. vitamins, fatty acids, minerals, and major polyphenols) were quantified. The first species investigated was the round-headed rampion (Phyteuma orbiculare L., Campanulaceae). The sweet flowers of the plant were consumed by shepherds as raw snacks, whereas nutty-tasting leaves (rosettes) were eaten as a salad. No phytochemical studies or biological data had been published for the entire genus Phyteuma. 23 substances including different polyphenols, fatty acids, and triterpenes were identified from dried aerial parts. Phytochemical investigations also revealed the presence of two novel saponins, phyteumosides A and B. The aglycon of phyteumoside A possessed an unprecedented skeleton that could be rationalized as an incompletely cyclized onoceroid triterpene, whereas that of phyteumoside B was a new 17-polypodene skeleton. Identification of these two substances was achieved by compilation of chemical and enzymatic hydrolyses, followed MS/MS, GC-MS, NMR and X-rays analyses. In addition to these two new substances, a new dimeric phenylpropanoid glycosylate derivative (tangshenoside VII) could be isolated and elucidated. Concerning the quantification of substances relevant for nutrition, Phyteuma orbiculare contained interesting amounts of ascorbic acid, beta-carotene, polyphenols, polyinsaturated fatty acids, calcium, magnesium and potassium This food plant, which possesses interesting nutritive properties and favorable breeding predispositions, could be an interesting candidate for further agronomic development. However, species of the same genus have a larger biomass and it was interesting to compare their phytochemical profile. HPLC-MS DAD analyses revealed similar metabolite profiles for P. spicatum, P. ovatum, and P. orbiculare but showed differences for P. hemisphaericum. The second plant to be investigated was a thistle, Cirsium spinosissimum (Asteraceae). Surrounding leaves and the pappus hairs were removed before consumption, and the receptacle was eaten in early summer time. Taste of the receptacle is similar to that of an artichoke, and its consistency is tender. A total of 20 substances including polyphenols, a monoterpene lactone, fatty acids and a spermine derivative were identified. Major polyphenols were linarin and pectolinarin and have been previously isolated from other Cirsium species. This plant contains vitamins and polyunsaturated fatty acids in low amounts, and an interesting level of potassium. Cirsium spinosissimum is not really convenient for further cultivation due to its spiny morphology. Other alpine edible plants selected during this work could be interesting with regard to their chemical composition, and for future breeding. They should be the main focus of further investigations. The establishment of alpine plants as new food crops would represent a diversification of the activities in mountain agriculture

Toward automatic tumor-stroma ratio assessment for survival analysis in colorectal cancer

In this paper, we present a fully automated system for tumor-stroma ratio (TSR) scoring in line with current recommendations for pathologists, based on tumor and tumor-adjacent stroma tissue detection. In order to evaluate the scoring system, we perform survival analysis on 221 whole slide image from colorectal cancer patients. We find that the whole slide image-level and region of interest-level TSR are statistically significant predictors of overall survival

New Acylated Flavonol Glycosides and a Phenolic Profile of Pritzelago alpina, a Forgotten Edible Alpine Plant

Thirteen acylated flavonoid glycosides, 1-13, including eleven new congeners, 3-13, were isolated from the aerial parts of Pritzelago alpina (Brassicaceae) by a combination of column chromatography on Sephadex LH-20, and preparative and semi-preparative HPLC. The structures were established by extensive NMR and MS experiments in combination with acid hydrolysis and sugar analysis by GC/MS. The new compounds were shown to be kaempferol and quercetin glycosides acylated for most of them by a branched short chain fatty acid or a hydroxycinnamic acid residue on the sugar portion. As shown by a HPLC-DAD analysis of a MeOH extract, these compounds are the main phenolic constituents in the aerial parts of the plant

Self-rule to adapt ::learning generalized features from sparsely-labeled data using unsupervised domain adaptation for colorectal cancer tissue phenotyping

Supervised learning is conditioned by the availability of labeled data, which are especially expensive to acquire in the field of medical image analysis. Making use of open-source data for pre-training or using domain adaptation can be a way to overcome this issue. However, pre-trained networks often fail to generalize to new test domains that are not distributed identically due to variations in tissue stainings, types, and textures. Additionally, current domain adaptation methods mainly rely on fully-labeled source datasets. In this work, we propose Self-Rule to Adapt (SRA) which takes advantage of self-supervised learning to perform domain adaptation and removes the burden of fully-labeled source datasets. SRA can effectively transfer the discriminative knowledge obtained from a few labeled source domain to a new target domain without requiring additional tissue annotations. Our method harnesses both domains’ structures by capturing visual similarity with intra-domain and cross-domain self-supervision. We show that our proposed method outperforms baselines across diverse domain adaptation settings and further validate our approach to our in-house clinical cohort

Impact of scanner variability on lymph node segmentation in computational pathology

Computer-aided diagnostics in histopathology are based on the digitization of glass slides. However, heterogeneity between the images generated by different slide scanners can unfavorably affect the performance of computational algorithms. Here, we evaluate the impact of scanner variability on lymph node segmentation due to its clinical importance in colorectal cancer diagnosis. 100 slides containing 276 lymph nodes were digitized using 4 different slide scanners, and 50 of the lymph nodes containing metastatic cancer cells. These 400 scans were subsequently annotated by 2 experienced pathologists to precisely label lymph node boundary. Three different segmentation methods were then applied and compared: Hematoxylin-channel-based thresholding (HCT), Hematoxylin-based active contours (HAC), and a convolution neural network (U-Net). Evaluation of U-Net trained from both a single scanner and an ensemble of all scanners was completed. Mosaic images based on representative tiles from a scanner were used as a reference image to normalize the new data from different test scanners to evaluate the performance of a pre-trained model. Fine-tuning was carried out by using weights of a model trained on one scanner to initialize model weights for other scanners. To evaluate the domain generalization, domain adversarial learning and stain mix-up augmentation were also implemented. Results show that fine-tuning and domain adversarial learning decreased the impact of scanner variability and greatly improved segmentation across scanners. Overall, U-Net with stain mix-up (Matthews correlation coefficient (MCC) = 0.87), domain adversarial learning (MCC = 0.86), and HAC (MCC = 0.87) were shown to outperform HCT (MCC = 0.81) for segmentation of lymph nodes when compared against the ground truth. The findings of this study should be considered for future algorithms applied in diagnostic routines