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Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge's improvement over the previous state-ofthe-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSIlevel results to foster the development of further methods for biomarker discovery.

Urine cytology is a test for the detection of high‐grade bladder cancer. In clinical practice, the pathologist would manually scan the sample under the microscope to locate atypical and malignant cells. They would assess the morphology of these cells to make a diagnosis. Accurate identification of atypical and malignant cells in urine cytology is a challenging task and is an essential part of identifying different diagnosis with low‐risk and high‐risk malignancy. Computer‐assisted identification of malignancy in urine cytology can be complementary to the clinicians for treatment management and in providing advice for carrying out further tests. In this study, we presented a method for identifying atypical and malignant cells followed by their profiling to predict the risk of diagnosis automatically. For cell detection and classification, we employed two different deep learning‐based approaches. Based on the best performing network predictions at the cell level, we identified low‐risk and high‐risk cases using the count of atypical cells and the total count of atypical and malignant cells. The area under the receiver operating characteristic (ROC) curve shows that a total count of atypical and malignant cells is comparably better at diagnosis as compared to the count of malignant cells only. We obtained area under the ROC curve with the count of malignant cells and the total count of atypical and malignant cells as 0.81 and 0.83, respectively. Our experiments also demonstrate that the digital risk could be a better predictor of the final histopathology‐based diagnosis. We also analyzed the variability in annotations at both cell and whole slide image level and also explored the possible inherent rationales behind this variability.

Classification of gigapixel Whole Slide Images (WSIs) is an important prediction task in the emerging area of computational pathology. There has been a surge of research in deep learning models for WSI classification with clinical applications such as cancer detection or prediction of molecular mutations from WSIs. Most methods require expensive and labor-intensive manual annotations by expert pathologists. Weakly supervised Multiple Instance Learning (MIL) methods have recently demonstrated excellent performance; however, they still require large slidelevel labeled training datasets that need a careful inspection of each slide by an expert pathologist. In this work, we propose a fully unsupervised WSI classification algorithm based on mutual transformer learning. Instances from gigapixel WSI (i.e., image patches) are transformed into a latent space and then inverse-transformed to the original space. Using the transformation loss, pseudo-labels are generated and cleaned using a transformer label-cleaner. The proposed transformer-based pseudo-label generation and cleaning modules mutually train each other iteratively in an unsupervised manner. A discriminative learning mechanism is introduced to improve normal versus cancerous instance labeling. In addition to unsupervised classification, we demonstrate the effectiveness of the proposed framework for weak supervision for cancer subtype classification as downstream analysis. Extensive experiments on four publicly available datasets show excellent performance compared to the state-of-the-art methods. We intend to make the source code of our algorithm publicly available soon.

A 68-year-old Asian woman presented with 2-week history of a painful left periareolar lump. Some of her medical history included: type II diabetes mellitus, chronic renal failure, moderate obesity, left chronic mastitis, mild hypercalcemia (2.60 mmol ⁄ L), and hypomagnesemia. Imaging and core biopsy of the breast diagnosed fat necrosis. The painful lump progressed into an ulcer over 4-month period. The second core biopsy of the breast lesion showed inflammatory changes only. Culture for Acid Fast Bacilli was negative while microscopy showed coliforms. The ulcer did not heal after several courses of antibiotics and wound dressing. Instead, it recurred and persisted for 7 months, progressing into necrosis. She underwent a vertical, central segmental wedge wide local excision with primary closure and satisfactory postoperative healing. The histology revealed extensive areas of ischemia, which were associated with abnormal small and medium size blood vessels (Fig. 1). The main abnormality of the blood vessel wall was calcification of the media (Fig. 2) with obliteration ⁄ hypertrophy of the intima and narrowing of the vessel lumen (Fig. 3). One of the vessels was completely occluded by thrombus (Fig. 4).In view of these findings, diagnosis of calciphylaxis in the breast was made, and previous biopsies were reviewed. These biopsies contained no blood vessels and suspicion of calciphylaxis could not have been raised earlier.Calciphylaxis occurring in the breast and resulting in chronic ulceration is uncommon. Only few cases have been reported in literature to date. It is characterized by calcification of small and medium sized vessels and is usually seen in patients with end stage chronic renal failure and secondary hyperparathyroidism. Our patient had normal parathyroid hormone level at time of presentation. Some cases have been reported in association with metastatic disease. The pathogenesis of calciphylaxis is not well understood