Efficient Open World Reasoning for Planning

We consider the problem of reasoning and planning with incomplete knowledge and deterministic actions. We introduce a knowledge representation scheme called PSIPLAN that can effectively represent incompleteness of an agent's knowledge while allowing for sound, complete and tractable entailment in domains where the set of all objects is either unknown or infinite. We present a procedure for state update resulting from taking an action in PSIPLAN that is correct, complete and has only polynomial complexity. State update is performed without considering the set of all possible worlds corresponding to the knowledge state. As a result, planning with PSIPLAN is done without direct manipulation of possible worlds. PSIPLAN representation underlies the PSIPOP planning algorithm that handles quantified goals with or without exceptions that no other domain independent planner has been shown to achieve. PSIPLAN has been implemented in Common Lisp and used in an application on planning in a collaborative interface.Comment: 39 pages, 13 figures. to appear in Logical Methods in Computer Scienc

Direct comparison between confocal and multiphoton microscopy for rapid histopathological evaluation of unfixed human breast tissue

Rapid histopathological examination of surgical specimen margins using fluorescence microscopy during breast conservation therapy has the potential to reduce the rate of positive margins on postoperative histopathology and the need for repeat surgeries. To assess the suitability of imaging modalities, we perform a direct comparison between confocal fluorescence microscopy and multiphoton microscopy for imaging unfixed tissue and compare to paraffin-embedded histology. An imaging protocol including dual channel detection of two contrast agents to implement virtual hematoxylin and eosin images is introduced that provides high quality imaging under both one and two photon excitation. Corresponding images of unfixed human breast tissue show that both confocal and multiphoton microscopy can reproduce the appearance of conventional histology without the need for physical sectioning. We further compare normal breast tissue and invasive cancer specimens imaged at multiple magnifications, and assess the effects of photobleaching for both modalities using the staining protocol. The results demonstrate that confocal fluorescence microscopy is a promising and cost-effective alternative to multiphoton microscopy for rapid histopathological evaluation of ex vivo breast tissue.National Institutes of Health (U.S.) (Grant R01-CA178636-02)National Institutes of Health (U.S.) (Grant R01-CA075289-18)National Institutes of Health (U.S.) (Grant F32-CA183400-03)United States. Air Force. Office of Scientific Research (Grant FA9550-12-1-0551)United States. Air Force. Office of Scientific Research (Grant FA9550-15-1-0473

Assessment of breast pathologies using nonlinear microscopy

Rapid intraoperative assessment of breast excision specimens is clinically important because up to 40% of patients undergoing breast-conserving cancer surgery require reexcision for positive or close margins. We demonstrate nonlinear microscopy (NLM) for the assessment of benign and malignant breast pathologies in fresh surgical specimens. A total of 179 specimens from 50 patients was imaged with NLM using rapid extrinsic nuclear staining with acridine orange and intrinsic second harmonic contrast generation from collagen. Imaging was performed on fresh, intact specimens without the need for fixation, embedding, and sectioning required for conventional histopathology. A visualization method to aid pathological interpretation is presented that maps NLM contrast from two-photon fluorescence and second harmonic signals to features closely resembling histopathology using hematoxylin and eosin staining. Mosaicking is used to overcome trade-offs between resolution and field of view, enabling imaging of subcellular features over square-centimeter specimens. After NLM examination, specimens were processed for standard paraffin-embedded histology using a protocol that coregistered histological sections to NLM images for paired assessment. Blinded NLM reading by three pathologists achieved 95.4% sensitivity and 93.3% specificity, compared with paraffin-embedded histology, for identifying invasive cancer and ductal carcinoma in situ versus benign breast tissue. Interobserver agreement was κ = 0.88 for NLM and κ = 0.89 for histology. These results show that NLM achieves high diagnostic accuracy, can be rapidly performed on unfixed specimens, and is a promising method for intraoperative margin assessment.National Institutes of Health (U.S.) (Grant R01-CA178636-01)National Institutes of Health (U.S.) (Grant R01-CA75289-16)United States. Air Force Office of Scientific Research (Grant FA9550-10-1-0551)United States. Air Force Office of Scientific Research (Grant FA9550-12-1-0499)National Institutes of Health (U.S.) (National Research Service Award Postdoctoral Fellowship F32-CA165484

Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study.

Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine pathology slides in colorectal cancer (CRC). However, current approaches rely on convolutional neural networks (CNNs) and have mostly been validated on small patient cohorts. Here, we develop a new transformer-based pipeline for end-to-end biomarker prediction from pathology slides by combining a pre-trained transformer encoder with a transformer network for patch aggregation. Our transformer-based approach substantially improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training and evaluating on a large multicenter cohort of over 13,000 patients from 16 colorectal cancer cohorts, we achieve a sensitivity of 0.99 with a negative predictive value of over 0.99 for prediction of microsatellite instability (MSI) on surgical resection specimens. We demonstrate that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem

Planning with Incomplete Knowledge and Limited Quantification

We present a new method for partial order planning in the STRIPS/SNLP style. Our contribution centers on how we drop the closed world assumption while adding a useful class of universally quantified propositions to the representation of states and actions. These quantified expressions allow expression of partially closed worlds, such as "block A has no other block on it", or "F is the only Tex file in directory D." In addition, we argue informally that the time complexity of our algorithm is no worse than traditional partial order planners that make the closed world assumption. STRIPS-style planning (Fikes & Nilsson 1971) is decidable only if we restrict the language to finitely many ground terms (Erol, Nau, & Subrahmanian 1992). STRIPS-style planning becomes NP-complete only when we bound the length of the plan being sought (Gupta & Nau 1991). Thus, planning is intractable in the general case. However, thanks to recent advances in applying stochastic search to propositional satisfiab..